人脸属性指的是根据给定的人脸判断其性别、年龄和表情等，当前在github上开源了一些相关的工作，大部分都是基于tensorflow的，还有一部分是keras，CVPR2015曾有一篇是用caffe做的.
CSDN
从0到1实现基于Tornado和Tensorflow的人脸、年龄、性别识别
github
https://github.com/GilLevi/AgeGenderDeepLearning:CVPR2015 caffe实现
https://github.com/dpressel/rude-carnie：CVPR2015对应的tensorflow实现
https://github.com/truongnmt/multi-task-learning: DEX: Deep EXpectation 实现
https://github.com/ZZUTK/Face-Aging-CAAE:CVPR2017 Age Progression/Regression by Conditional Adversarial Autoencoder
https://github.com/BoyuanJiang/Age-Gender-Estimate-TF:
https://github.com/zZyan/race_gender_recognition:gender Accuracy: 0.951493,race Accuracy: 0.87557212
https://github.com/yu4u/age-gender-estimation:UTKFace训练
https://github.com/jocialiang/gender_classifier:性别识别全流程实现 94% accuracy
https://github.com/oarriaga/face_classification:表情识别
https://github.com/shamangary/SSR-Net:年龄识别
https://github.com/b02901145/SSR-Net_megaage-asian：亚洲人优化
https://github.com/yu4u/age-gender-estimation：年龄和性别识别
https://github.com/isseu/emotion-recognition-neural-networks:表情66% with fer2013，性别96% with imdb.
https://github.com/zealerww/gender_age_classification:91% accuracy in gender and 55% in age
https://github.com/vipstone/faceai:gender 96%
https://github.com/ybch14/Facial-Expression-Recognition-ResNet66.7% on fer2013 with resnet50
https://github.com/JostineHo/mememoji:58% with 动画展示
https://github.com/mangorocoro/racedetector:种族识别
https://github.com/HectorAnadon/Face-expression-and-ethnic-recognition:表情 72% accuracy ，种族95% accuracy
https://github.com/XiuweiHe/EmotionClassifier： 66% on fer2013 with mini_XCEPTION
https://github.com/truongnmt/multi-task-learning:多任务学习
useless
https://github.com/StevenKe8080/recognition_gender:使用爬取的图片训练
https://github.com/zonetrooper32/AgeEstimateAdience:
https://github.com/OValery16/gender-age-classification:
数据库
UTKFace：over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc.
SCUT-FBP5500:5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (facial landmarks, beauty scores in 5 scales, beauty score distribution), which allows different computational model with different facial beauty prediction paradigms, such as appearance-based/shape-based facial beauty classification/regression/ranking model for male/female of Asian/Caucasian
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202,599 number of face images, and
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5 landmark locations, 40 binary attributes annotations per image.
APPA-REAL ：视觉年龄估计，7,591张带有实际年龄和视觉年龄标注的图片，分为 4113 train, 1500 valid and 1978 test images,大小:844M
AFAD Dataset： Asian Face Age Dataset，more than 160K facial images and the corresponding age and gender labels.暂未开放下载
FER+ :微软重新标注的fer2013，表情识别比赛数据
NKI:GENKI数据集是由加利福尼亚大学的机器概念实验室收集。该数据集包含GENKI-R2009a,GENKI-4K,GENKI-SZSL三个部分。GENKI-R2009a包含11159个图像，GENKI-4K包含4000个图像，分为“笑”和“不笑”两种，每个图片的人脸的尺度大小，姿势，光照变化，头的转动等都不一样，专门用于做笑脸识别。GENKI-SZSL包含3500个图像，这些图像包括广泛的背景，光照条件，地理位置，个人身份和种族等