summaryrefslogtreecommitdiff
path: root/site/datasets/unknown/adience.json
diff options
context:
space:
mode:
Diffstat (limited to 'site/datasets/unknown/adience.json')
-rw-r--r--site/datasets/unknown/adience.json2
1 files changed, 1 insertions, 1 deletions
diff --git a/site/datasets/unknown/adience.json b/site/datasets/unknown/adience.json
index 80ad794a..ee1ba09f 100644
--- a/site/datasets/unknown/adience.json
+++ b/site/datasets/unknown/adience.json
@@ -1 +1 @@
-{"id": "1be498d4bbc30c3bfd0029114c784bc2114d67c0", "citations": [{"id": "12e4545d07e1793df87520f384b37a015815d2f7", "title": "Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging", "year": "2018", "pdf": []}, {"id": "d9c0310203179d5328c4f1475fa4d68c5f0c7324", "title": "Face Analysis in the Wild", "year": "2017", "pdf": []}, {"id": "be72b20247fb4dc4072d962ced77ed89aa40372f", "title": "Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.07718.pdf"]}, {"id": "8879083463a471898ff9ed9403b84db277be5bf6", "title": "Regression Facial Attribute Classification via simultaneous dictionary learning", "year": "2017", "pdf": []}, {"id": "633c851ebf625ad7abdda2324e9de093cf623141", "title": "Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database", "year": "2017", "pdf": ["http://sergioescalera.com/wp-content/uploads/2017/05/APPA-REAL-Slides.pdf", "http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01362.pdf"]}, {"id": "ec0104286c96707f57df26b4f0a4f49b774c486b", "title": "An Ensemble CNN2ELM for Age Estimation", "year": "2018", "pdf": ["http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf"]}, {"id": "7cee802e083c5e1731ee50e731f23c9b12da7d36", "title": "2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.02181.pdf"]}, {"id": "b8b9cef0938975c5b640b7ada4e3dea6c06d64e9", "title": "Metric-Promoted Siamese Network for Gender Classification", "year": "2017", "pdf": []}, {"id": "30457461333c8797457c18636732327e6dde1d04", "title": "Gender classification system for half face images using multi manifold discriminant analysis", "year": "2017", "pdf": []}, {"id": "0dccc881cb9b474186a01fd60eb3a3e061fa6546", "title": "Effective face frontalization in unconstrained images", "year": "2015", "pdf": ["https://arxiv.org/pdf/1411.7964.pdf"]}, {"id": "c95d8b9bddd76b8c83c8745747e8a33feedf3941", "title": "Image Ordinal Classification and Understanding: Grid Dropout with Masking Label", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.02901.pdf"]}, {"id": "cb522b2e16b11dde48203bef97131ddca3cdaebd", "title": "Fusion of Domain-Specific and Trainable Features for Gender Recognition From Face Images", "year": "2018", "pdf": []}, {"id": "c8adbe00b5661ab9b3726d01c6842c0d72c8d997", "title": "Deep Architectures for Face Attributes", "year": "2016", "pdf": ["https://arxiv.org/pdf/1609.09018.pdf"]}, {"id": "635158d2da146e9de559d2742a2fa234e06b52db", "title": "Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns", "year": "2015", "pdf": []}, {"id": "841c99e887eb262e397fdf5b0490a2ae6c82d6b5", "title": "Feature extraction for facial age estimation: A survey", "year": "2016", "pdf": []}, {"id": "e5563a0d6a2312c614834dc784b5cc7594362bff", "title": "Real-Time Demographic Profiling from Face Imagery with Fisher Vectors", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/e556/3a0d6a2312c614834dc784b5cc7594362bff.pdf"]}, {"id": "5e39deb4bff7b887c8f3a44dfe1352fbcde8a0bd", "title": "Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!", "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.06221.pdf"]}, {"id": "b972683d702a65d3ee7a25bc931a5890d1072b6b", "title": "Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers", "year": "2018", "pdf": []}, {"id": "50ff21e595e0ebe51ae808a2da3b7940549f4035", "title": "Age Group and Gender Estimation in the Wild With Deep RoR Architecture", "year": "2017", "pdf": ["https://arxiv.org/pdf/1710.02985.pdf"]}, {"id": "0dd74bbda5dd3d9305636d4b6f0dad85d6e19572", "title": "Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach", "year": "2018", "pdf": ["https://arxiv.org/pdf/1706.00906.pdf"]}, {"id": "f77c9bf5beec7c975584e8087aae8d679664a1eb", "title": "Local Deep Neural Networks for Age and Gender Classification", "year": "2017", "pdf": ["https://arxiv.org/pdf/1703.08497.pdf"]}, {"id": "be0a0e563445119b82d664d370e646e53e69a4c5", "title": "Age and gender classification from speech and face images by jointly fine-tuned deep neural networks", "year": "2017", "pdf": []}, {"id": "03f3bde03f83c3ff4f346d761fde4ce031dd4c69", "title": "Deep Models Calibration with Bayesian Neural Networks", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/03f3/bde03f83c3ff4f346d761fde4ce031dd4c69.pdf"]}, {"id": "af6e351d58dba0962d6eb1baf4c9a776eb73533f", "title": "How to Train Your Deep Neural Network with Dictionary Learning", "year": "2016", "pdf": ["https://arxiv.org/pdf/1612.07454.pdf"]}, {"id": "6dcf418c778f528b5792104760f1fbfe90c6dd6a", "title": "AgeDB: The First Manually Collected, In-the-Wild Age Database", "year": "2017", "pdf": ["https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf"]}, {"id": "772a30f1a7a3071e5ce6ad4b0dbddc67889f5873", "title": "FDAR-Net: Joint Convolutional Neural Networks for Face Detection and Attribute Recognition", "year": "2016", "pdf": []}, {"id": "e8b56ed34ece9b1739fff0df6af3b65390c468d3", "title": "Human injected by Botox age estimation based on active shape models, speed up robust features, and support vector machine", "year": "2016", "pdf": []}, {"id": "1fc88451a83f088ce028a0f715b9f9b600f4bd1c", "title": "Facial Attribute Recognition by Recurrent Learning With Visual Fixation.", "year": "2018", "pdf": []}, {"id": "e295c1aa47422eb35123053038e62e9aa50a2e3a", "title": "ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Escalera_ChaLearn_Looking_at_ICCV_2015_paper.pdf"]}, {"id": "e8951cc76af80da43e3528fe6d984071f17f57e7", "title": "Online Cost Efficient Customer Recognition System for Retail Analytics", "year": "2017", "pdf": []}, {"id": "29db16efc3b378c50511f743e5197a4c0b9e902f", "title": "Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Kuang_Deeply_Learned_Rich_ICCV_2015_paper.pdf"]}, {"id": "bc749f0e81eafe9e32d56336750782f45d82609d", "title": "Combination of Texture and Geometric Features for Age Estimation in Face Images", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/bc74/9f0e81eafe9e32d56336750782f45d82609d.pdf"]}, {"id": "43836d69f00275ba2f3d135f0ca9cf88d1209a87", "title": "Effective hyperparameter optimization using Nelder-Mead method in deep learning", "year": "2017", "pdf": ["https://ipsjcva.springeropen.com/track/pdf/10.1186/s41074-017-0030-7?site=ipsjcva.springeropen.com"]}, {"id": "9939498315777b40bed9150d8940fc1ac340e8ba", "title": "ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016", "year": "2016", "pdf": ["http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w18/papers/Escalera_ChaLearn_Looking_at_CVPR_2016_paper.pdf"]}, {"id": "0435a34e93b8dda459de49b499dd71dbb478dc18", "title": "VEGAC: Visual Saliency-based Age, Gender, and Facial Expression Classification Using Convolutional Neural Networks", "year": "2018", "pdf": []}, {"id": "7173871866fc7e555e9123d1d7133d20577054e8", "title": "Simultaneous Adversarial Training - Learn from Others Mistakes", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.08108.pdf"]}, {"id": "341ed69a6e5d7a89ff897c72c1456f50cfb23c96", "title": "DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network", "year": "2017", "pdf": ["https://arxiv.org/pdf/1702.04280.pdf"]}, {"id": "8355d095d3534ef511a9af68a3b2893339e3f96b", "title": "DEX: Deep EXpectation of Apparent Age from a Single Image", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.pdf", "http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01229.pdf", "https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/105213/eth-48173-01.pdf"]}, {"id": "1135a818b756b057104e45d976546970ba84e612", "title": "Age, Gender, and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset", "year": "2017", "pdf": []}, {"id": "5a4ec5c79f3699ba037a5f06d8ad309fb4ee682c", "title": "Automatic age and gender classification using supervised appearance model", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/2e36/a706bbec0f1adb7484e5d7416c3e612f43a1.pdf"]}, {"id": "7aa4c16a8e1481629f16167dea313fe9256abb42", "title": "Multi-task learning for face identification and attribute estimation", "year": "2017", "pdf": ["http://mirlab.org/conference_papers/International_Conference/ICASSP%202017/pdfs/0002981.pdf"]}, {"id": "0deea943ac4dc1be822c02f97d0c6c97e201ba8d", "title": "Age category estimation using matching convolutional neural network", "year": "2018", "pdf": []}, {"id": "305346d01298edeb5c6dc8b55679e8f60ba97efb", "title": "Fine-Grained Face Annotation Using Deep Multi-Task CNN", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/3053/46d01298edeb5c6dc8b55679e8f60ba97efb.pdf"]}, {"id": "4562ea84ebfc8d9864e943ed9e44d35997bbdf43", "title": "Small Sample Deep Learning for Newborn Gestational Age Estimation", "year": "2017", "pdf": ["http://eprints.nottingham.ac.uk/40828/1/automatic-gestational-age.pdf"]}, {"id": "d4d1ac1cfb2ca703c4db8cc9a1c7c7531fa940f9", "title": "Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction", "year": "2017", "pdf": []}, {"id": "7361b900018f22e37499443643be1ff9d20edfd6", "title": "Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data", "year": "2017", "pdf": []}, {"id": "166186e551b75c9b5adcc9218f0727b73f5de899", "title": "Automatic Age and Gender Recognition in Human Face Image Dataset using Convolutional Neural Network System", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/1661/86e551b75c9b5adcc9218f0727b73f5de899.pdf"]}, {"id": "9755554b13103df634f9b1ef50a147dd02eab02f", "title": "How Transferable Are CNN-Based Features for Age and Gender Classification?", "year": "2016", "pdf": ["https://arxiv.org/pdf/1610.00134.pdf"]}, {"id": "f4373f5631329f77d85182ec2df6730cbd4686a9", "title": "Recognizing Gender from Human Facial Regions using Genetic Algorithm", "year": "2017", "pdf": ["https://arxiv.org/pdf/1712.01661.pdf"]}, {"id": "2b60fe300735ea7c63f91c1121e89ba66040b833", "title": "A study on face recognition techniques with age and gender classification", "year": "2017", "pdf": []}, {"id": "d278e020be85a1ccd90aa366b70c43884dd3f798", "title": "Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.11191.pdf"]}, {"id": "0a325d70cc381b136a8f4e471b406cda6d27668c", "title": "A flexible hierarchical approach for facial age estimation based on multiple features", "year": "2016", "pdf": ["https://www.etsmtl.ca/Unites-de-recherche/LIVIA/Recherche-et-innovation/Publications/Publications-2016/F1b-PR2016.pdf"]}, {"id": "775c15a5dfca426d53c634668e58dd5d3314ea89", "title": "Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/775c/15a5dfca426d53c634668e58dd5d3314ea89.pdf"]}, {"id": "2cbb4a2f8fd2ddac86f8804fd7ffacd830a66b58", "title": "Age and gender classification using convolutional neural networks", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/papers/Levi_Age_and_Gender_2015_CVPR_paper.pdf", "http://www.openu.ac.il/home/hassner/projects/cnn_agegender/CNN_AgeGenderEstimation.pdf"]}, {"id": "f0ba5c89094b15469f95fd2a05a46b68b8faf1ca", "title": "Recognizing images across age progressions: A comprehensive review", "year": "2015", "pdf": []}, {"id": "5fb5d9389e2a2a4302c81bcfc068a4c8d4efe70c", "title": "Multiple Facial Attributes Estimation Based on Weighted Heterogeneous Learning", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/5fb5/d9389e2a2a4302c81bcfc068a4c8d4efe70c.pdf"]}, {"id": "81e628a23e434762b1208045919af48dceb6c4d2", "title": "Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.07320.pdf"]}, {"id": "5c09d905f6d4f861624821bf9dfe2aae29137e9c", "title": "Women Also Snowboard: Overcoming Bias in Captioning Models", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.00517.pdf"]}, {"id": "732e8d8f5717f8802426e1b9debc18a8361c1782", "title": "Unimodal Probability Distributions for Deep Ordinal Classification", "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/732e/8d8f5717f8802426e1b9debc18a8361c1782.pdf"]}, {"id": "cca476114c48871d05537abb303061de5ab010d6", "title": "A compact deep convolutional neural network architecture for video based age and gender estimation", "year": "2016", "pdf": []}, {"id": "0cfca73806f443188632266513bac6aaf6923fa8", "title": "Predictive Uncertainty in Large Scale Classification using Dropout - Stochastic Gradient Hamiltonian Monte Carlo", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.04756.pdf"]}, {"id": "d57dca4413ad4f33c97ae06a5a7fc86dc5a75f8b", "title": "Gender recognition: Methods, datasets and results", "year": "2015", "pdf": ["http://iplab.dmi.unict.it/sites/default/files/_11.pdf"]}, {"id": "00a38ebce124879738b04ffc1536018e75399193", "title": "Convolutional neural network for age classification from smart-phone based ocular images", "year": "2017", "pdf": []}, {"id": "e7b6887cd06d0c1aa4902335f7893d7640aef823", "title": "Modeling of facial aging and kinship: A survey", "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.04636.pdf"]}, {"id": "f4003cbbff3b3d008aa64c76fed163c10d9c68bd", "title": "Compass local binary patterns for gender recognition of facial photographs and sketches", "year": "2016", "pdf": []}, {"id": "c254b4c0f6d5a5a45680eb3742907ec93c3a222b", "title": "A Fusion-based Gender Recognition Method Using Facial Images", "year": "2018", "pdf": ["https://arxiv.org/pdf/1711.06451.pdf"]}, {"id": "321db1059032b828b223ca30f3304257f0c41e4c", "title": "Comparative evaluation of age classification from facial images", "year": "2015", "pdf": []}, {"id": "cfdc632adcb799dba14af6a8339ca761725abf0a", "title": "Probabilistic Formulations of Regression with Mixed Guidance", "year": "2016", "pdf": ["https://arxiv.org/pdf/1804.01575.pdf"]}, {"id": "1277b1b8b609a18b94e4907d76a117c9783a5373", "title": "VirtualIdentity: Privacy preserving user profiling", "year": "2016", "pdf": ["https://arxiv.org/pdf/1808.10151.pdf"]}, {"id": "b839bc95794dc65340b6e5fea098fa6e6ea5e430", "title": "Soft Biometrics in Online Social Networks: A Case Study on Twitter User Gender Recognition", "year": "2017", "pdf": []}, {"id": "42a5dc91852c8c14ed5f4c3b451c9dc98348bc02", "title": "A Data Augmentation Methodology to Improve Age Estimation Using Convolutional Neural Networks", "year": "2016", "pdf": []}, {"id": "fffefc1fb840da63e17428fd5de6e79feb726894", "title": "Fine-Grained Age Estimation in the wild with Attention LSTM Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.10445.pdf"]}, {"id": "c9c2de3628be7e249722b12911bebad84b567ce6", "title": "Age and gender recognition in the wild with deep attention", "year": "2017", "pdf": []}, {"id": "ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd", "title": "Age group classification in the wild with deep RoR architecture", "year": "2017", "pdf": []}, {"id": "cdae8e9cc9d605856cf5709b2fdf61f722d450c1", "title": "Deep Learning for Biometrics : A Survey KALAIVANI SUNDARARAJAN", "year": "2018", "pdf": []}, {"id": "ab3fcd9d5fbd2d0ad48fba4005899cf13e08d07e", "title": "Evaluating Automated Facial Age Estimation Techniques for Digital Forensics", "year": "2018", "pdf": ["https://forensicsandsecurity.com/papers/EvaluatingFacialAgeEstimation.pdf"]}]} \ No newline at end of file
+{"id": "1be498d4bbc30c3bfd0029114c784bc2114d67c0", "citations": [{"id": "12e4545d07e1793df87520f384b37a015815d2f7", "title": "Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging", "year": "2018", "pdf": []}, {"id": "d9c0310203179d5328c4f1475fa4d68c5f0c7324", "title": "Face Analysis in the Wild", "year": "2017", "pdf": []}, {"id": "be72b20247fb4dc4072d962ced77ed89aa40372f", "title": "Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.07718.pdf"]}, {"id": "8879083463a471898ff9ed9403b84db277be5bf6", "title": "Regression Facial Attribute Classification via simultaneous dictionary learning", "year": "2017", "pdf": []}, {"id": "633c851ebf625ad7abdda2324e9de093cf623141", "title": "Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database", "year": "2017", "pdf": ["http://sergioescalera.com/wp-content/uploads/2017/05/APPA-REAL-Slides.pdf", "http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01362.pdf"]}, {"id": "ec0104286c96707f57df26b4f0a4f49b774c486b", "title": "An Ensemble CNN2ELM for Age Estimation", "year": "2018", "pdf": ["http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf"]}, {"id": "7cee802e083c5e1731ee50e731f23c9b12da7d36", "title": "2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.02181.pdf"]}, {"id": "b8b9cef0938975c5b640b7ada4e3dea6c06d64e9", "title": "Metric-Promoted Siamese Network for Gender Classification", "year": "2017", "pdf": []}, {"id": "30457461333c8797457c18636732327e6dde1d04", "title": "Gender classification system for half face images using multi manifold discriminant analysis", "year": "2017", "pdf": []}, {"id": "0dccc881cb9b474186a01fd60eb3a3e061fa6546", "title": "Effective face frontalization in unconstrained images", "year": "2015", "pdf": ["https://arxiv.org/pdf/1411.7964.pdf"]}, {"id": "c95d8b9bddd76b8c83c8745747e8a33feedf3941", "title": "Image Ordinal Classification and Understanding: Grid Dropout with Masking Label", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.02901.pdf"]}, {"id": "cb522b2e16b11dde48203bef97131ddca3cdaebd", "title": "Fusion of Domain-Specific and Trainable Features for Gender Recognition From Face Images", "year": "2018", "pdf": []}, {"id": "c8adbe00b5661ab9b3726d01c6842c0d72c8d997", "title": "Deep Architectures for Face Attributes", "year": "2016", "pdf": ["https://arxiv.org/pdf/1609.09018.pdf"]}, {"id": "635158d2da146e9de559d2742a2fa234e06b52db", "title": "Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns", "year": "2015", "pdf": []}, {"id": "841c99e887eb262e397fdf5b0490a2ae6c82d6b5", "title": "Feature extraction for facial age estimation: A survey", "year": "2016", "pdf": []}, {"id": "e5563a0d6a2312c614834dc784b5cc7594362bff", "title": "Real-Time Demographic Profiling from Face Imagery with Fisher Vectors", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/e556/3a0d6a2312c614834dc784b5cc7594362bff.pdf"]}, {"id": "5e39deb4bff7b887c8f3a44dfe1352fbcde8a0bd", "title": "Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!", "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.06221.pdf"]}, {"id": "b972683d702a65d3ee7a25bc931a5890d1072b6b", "title": "Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers", "year": "2018", "pdf": []}, {"id": "50ff21e595e0ebe51ae808a2da3b7940549f4035", "title": "Age Group and Gender Estimation in the Wild With Deep RoR Architecture", "year": "2017", "pdf": ["https://arxiv.org/pdf/1710.02985.pdf"]}, {"id": "0dd74bbda5dd3d9305636d4b6f0dad85d6e19572", "title": "Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach", "year": "2018", "pdf": ["https://arxiv.org/pdf/1706.00906.pdf"]}, {"id": "f77c9bf5beec7c975584e8087aae8d679664a1eb", "title": "Local Deep Neural Networks for Age and Gender Classification", "year": "2017", "pdf": ["https://arxiv.org/pdf/1703.08497.pdf"]}, {"id": "be0a0e563445119b82d664d370e646e53e69a4c5", "title": "Age and gender classification from speech and face images by jointly fine-tuned deep neural networks", "year": "2017", "pdf": []}, {"id": "03f3bde03f83c3ff4f346d761fde4ce031dd4c69", "title": "Deep Models Calibration with Bayesian Neural Networks", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/03f3/bde03f83c3ff4f346d761fde4ce031dd4c69.pdf"]}, {"id": "af6e351d58dba0962d6eb1baf4c9a776eb73533f", "title": "How to Train Your Deep Neural Network with Dictionary Learning", "year": "2016", "pdf": ["https://arxiv.org/pdf/1612.07454.pdf"]}, {"id": "6dcf418c778f528b5792104760f1fbfe90c6dd6a", "title": "AgeDB: The First Manually Collected, In-the-Wild Age Database", "year": "2017", "pdf": ["https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf"]}, {"id": "772a30f1a7a3071e5ce6ad4b0dbddc67889f5873", "title": "FDAR-Net: Joint Convolutional Neural Networks for Face Detection and Attribute Recognition", "year": "2016", "pdf": []}, {"id": "e8b56ed34ece9b1739fff0df6af3b65390c468d3", "title": "Human injected by Botox age estimation based on active shape models, speed up robust features, and support vector machine", "year": "2016", "pdf": []}, {"id": "1fc88451a83f088ce028a0f715b9f9b600f4bd1c", "title": "Facial Attribute Recognition by Recurrent Learning With Visual Fixation.", "year": "2018", "pdf": []}, {"id": "e295c1aa47422eb35123053038e62e9aa50a2e3a", "title": "ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Escalera_ChaLearn_Looking_at_ICCV_2015_paper.pdf"]}, {"id": "e8951cc76af80da43e3528fe6d984071f17f57e7", "title": "Online Cost Efficient Customer Recognition System for Retail Analytics", "year": "2017", "pdf": []}, {"id": "29db16efc3b378c50511f743e5197a4c0b9e902f", "title": "Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Kuang_Deeply_Learned_Rich_ICCV_2015_paper.pdf"]}, {"id": "bc749f0e81eafe9e32d56336750782f45d82609d", "title": "Combination of Texture and Geometric Features for Age Estimation in Face Images", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/bc74/9f0e81eafe9e32d56336750782f45d82609d.pdf"]}, {"id": "43836d69f00275ba2f3d135f0ca9cf88d1209a87", "title": "Effective hyperparameter optimization using Nelder-Mead method in deep learning", "year": "2017", "pdf": ["https://ipsjcva.springeropen.com/track/pdf/10.1186/s41074-017-0030-7?site=ipsjcva.springeropen.com"]}, {"id": "9939498315777b40bed9150d8940fc1ac340e8ba", "title": "ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016", "year": "2016", "pdf": ["http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w18/papers/Escalera_ChaLearn_Looking_at_CVPR_2016_paper.pdf"]}, {"id": "0435a34e93b8dda459de49b499dd71dbb478dc18", "title": "VEGAC: Visual Saliency-based Age, Gender, and Facial Expression Classification Using Convolutional Neural Networks", "year": "2018", "pdf": []}, {"id": "7173871866fc7e555e9123d1d7133d20577054e8", "title": "Simultaneous Adversarial Training - Learn from Others Mistakes", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.08108.pdf"]}, {"id": "341ed69a6e5d7a89ff897c72c1456f50cfb23c96", "title": "DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network", "year": "2017", "pdf": ["https://arxiv.org/pdf/1702.04280.pdf"]}, {"id": "8355d095d3534ef511a9af68a3b2893339e3f96b", "title": "DEX: Deep EXpectation of Apparent Age from a Single Image", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.pdf", "http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01229.pdf", "https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/105213/eth-48173-01.pdf"]}, {"id": "1135a818b756b057104e45d976546970ba84e612", "title": "Age, Gender, and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset", "year": "2017", "pdf": []}, {"id": "5a4ec5c79f3699ba037a5f06d8ad309fb4ee682c", "title": "Automatic age and gender classification using supervised appearance model", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/2e36/a706bbec0f1adb7484e5d7416c3e612f43a1.pdf"]}, {"id": "7aa4c16a8e1481629f16167dea313fe9256abb42", "title": "Multi-task learning for face identification and attribute estimation", "year": "2017", "pdf": ["http://mirlab.org/conference_papers/International_Conference/ICASSP%202017/pdfs/0002981.pdf"]}, {"id": "0deea943ac4dc1be822c02f97d0c6c97e201ba8d", "title": "Age category estimation using matching convolutional neural network", "year": "2018", "pdf": []}, {"id": "305346d01298edeb5c6dc8b55679e8f60ba97efb", "title": "Fine-Grained Face Annotation Using Deep Multi-Task CNN", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/3053/46d01298edeb5c6dc8b55679e8f60ba97efb.pdf"]}, {"id": "4562ea84ebfc8d9864e943ed9e44d35997bbdf43", "title": "Small Sample Deep Learning for Newborn Gestational Age Estimation", "year": "2017", "pdf": ["http://eprints.nottingham.ac.uk/40828/1/automatic-gestational-age.pdf"]}, {"id": "d4d1ac1cfb2ca703c4db8cc9a1c7c7531fa940f9", "title": "Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction", "year": "2017", "pdf": []}, {"id": "7361b900018f22e37499443643be1ff9d20edfd6", "title": "Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data", "year": "2017", "pdf": []}, {"id": "166186e551b75c9b5adcc9218f0727b73f5de899", "title": "Automatic Age and Gender Recognition in Human Face Image Dataset using Convolutional Neural Network System", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/1661/86e551b75c9b5adcc9218f0727b73f5de899.pdf"]}, {"id": "9755554b13103df634f9b1ef50a147dd02eab02f", "title": "How Transferable Are CNN-Based Features for Age and Gender Classification?", "year": "2016", "pdf": ["https://arxiv.org/pdf/1610.00134.pdf"]}, {"id": "f4373f5631329f77d85182ec2df6730cbd4686a9", "title": "Recognizing Gender from Human Facial Regions using Genetic Algorithm", "year": "2017", "pdf": ["https://arxiv.org/pdf/1712.01661.pdf"]}, {"id": "2b60fe300735ea7c63f91c1121e89ba66040b833", "title": "A study on face recognition techniques with age and gender classification", "year": "2017", "pdf": []}, {"id": "d278e020be85a1ccd90aa366b70c43884dd3f798", "title": "Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.11191.pdf"]}, {"id": "0a325d70cc381b136a8f4e471b406cda6d27668c", "title": "A flexible hierarchical approach for facial age estimation based on multiple features", "year": "2016", "pdf": ["https://www.etsmtl.ca/Unites-de-recherche/LIVIA/Recherche-et-innovation/Publications/Publications-2016/F1b-PR2016.pdf"]}, {"id": "775c15a5dfca426d53c634668e58dd5d3314ea89", "title": "Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild", "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/775c/15a5dfca426d53c634668e58dd5d3314ea89.pdf"]}, {"id": "2cbb4a2f8fd2ddac86f8804fd7ffacd830a66b58", "title": "Age and gender classification using convolutional neural networks", "year": "2015", "pdf": ["http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/papers/Levi_Age_and_Gender_2015_CVPR_paper.pdf", "http://www.openu.ac.il/home/hassner/projects/cnn_agegender/CNN_AgeGenderEstimation.pdf"]}, {"id": "f0ba5c89094b15469f95fd2a05a46b68b8faf1ca", "title": "Recognizing images across age progressions: A comprehensive review", "year": "2015", "pdf": []}, {"id": "5fb5d9389e2a2a4302c81bcfc068a4c8d4efe70c", "title": "Multiple Facial Attributes Estimation Based on Weighted Heterogeneous Learning", "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/5fb5/d9389e2a2a4302c81bcfc068a4c8d4efe70c.pdf"]}, {"id": "81e628a23e434762b1208045919af48dceb6c4d2", "title": "Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.07320.pdf"]}, {"id": "5c09d905f6d4f861624821bf9dfe2aae29137e9c", "title": "Women Also Snowboard: Overcoming Bias in Captioning Models", "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.00517.pdf"]}, {"id": "732e8d8f5717f8802426e1b9debc18a8361c1782", "title": "Unimodal Probability Distributions for Deep Ordinal Classification", "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/732e/8d8f5717f8802426e1b9debc18a8361c1782.pdf"]}, {"id": "cca476114c48871d05537abb303061de5ab010d6", "title": "A compact deep convolutional neural network architecture for video based age and gender estimation", "year": "2016", "pdf": []}, {"id": "0cfca73806f443188632266513bac6aaf6923fa8", "title": "Predictive Uncertainty in Large Scale Classification using Dropout - Stochastic Gradient Hamiltonian Monte Carlo", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.04756.pdf"]}, {"id": "00a38ebce124879738b04ffc1536018e75399193", "title": "Convolutional neural network for age classification from smart-phone based ocular images", "year": "2017", "pdf": []}, {"id": "e7b6887cd06d0c1aa4902335f7893d7640aef823", "title": "Modeling of facial aging and kinship: A survey", "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.04636.pdf"]}, {"id": "f4003cbbff3b3d008aa64c76fed163c10d9c68bd", "title": "Compass local binary patterns for gender recognition of facial photographs and sketches", "year": "2016", "pdf": []}, {"id": "c254b4c0f6d5a5a45680eb3742907ec93c3a222b", "title": "A Fusion-based Gender Recognition Method Using Facial Images", "year": "2018", "pdf": ["https://arxiv.org/pdf/1711.06451.pdf"]}, {"id": "321db1059032b828b223ca30f3304257f0c41e4c", "title": "Comparative evaluation of age classification from facial images", "year": "2015", "pdf": []}, {"id": "cfdc632adcb799dba14af6a8339ca761725abf0a", "title": "Probabilistic Formulations of Regression with Mixed Guidance", "year": "2016", "pdf": ["https://arxiv.org/pdf/1804.01575.pdf"]}, {"id": "1277b1b8b609a18b94e4907d76a117c9783a5373", "title": "VirtualIdentity: Privacy preserving user profiling", "year": "2016", "pdf": ["https://arxiv.org/pdf/1808.10151.pdf"]}, {"id": "b839bc95794dc65340b6e5fea098fa6e6ea5e430", "title": "Soft Biometrics in Online Social Networks: A Case Study on Twitter User Gender Recognition", "year": "2017", "pdf": []}, {"id": "42a5dc91852c8c14ed5f4c3b451c9dc98348bc02", "title": "A Data Augmentation Methodology to Improve Age Estimation Using Convolutional Neural Networks", "year": "2016", "pdf": []}, {"id": "fffefc1fb840da63e17428fd5de6e79feb726894", "title": "Fine-Grained Age Estimation in the wild with Attention LSTM Networks", "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.10445.pdf"]}, {"id": "c9c2de3628be7e249722b12911bebad84b567ce6", "title": "Age and gender recognition in the wild with deep attention", "year": "2017", "pdf": []}, {"id": "ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd", "title": "Age group classification in the wild with deep RoR architecture", "year": "2017", "pdf": []}, {"id": "cdae8e9cc9d605856cf5709b2fdf61f722d450c1", "title": "Deep Learning for Biometrics : A Survey KALAIVANI SUNDARARAJAN", "year": "2018", "pdf": []}, {"id": "ab3fcd9d5fbd2d0ad48fba4005899cf13e08d07e", "title": "Evaluating Automated Facial Age Estimation Techniques for Digital Forensics", "year": "2018", "pdf": ["https://forensicsandsecurity.com/papers/EvaluatingFacialAgeEstimation.pdf"]}]} \ No newline at end of file