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.
原
人脸属性分析--性别、年龄和表情识别,及其相关的数据集合
CSDN
从0到1实现基于Tornado和Tensorflow的人脸、年龄、性别识别
基于caffe的表情识别
tensorflow练习12：利用图片预测年龄与性别
怎样用Keras识别人物面部表情
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:使用inception v1同时预测性别和年龄，受限于使用的dlib检测器，效果并不是很好
https://github.com/zZyan/race_gender_recognition:gender Accuracy: 0.951493,race Accuracy: 0.87557212
https://github.com/yu4u/age-gender-estimation:UTKFace库 WideResNet,64*64输入 keras模型，MAE 4.06
https://github.com/dandynaufaldi/Agendernetkeras实现了inception v3,mobilenet和ssr在imdb、utkface等的训练
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/wondonghyeon/face-classification:性别和种族识别
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/XiuweiHe/EmotionClassifier:表情识别
https://github.com/HectorAnadon/Face-expression-and-ethnic-recognition:表情和种族识别
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
CelebA:标注了40个属性，第21个属性为性别
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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
| Datasets | Description | Links | Key features | Publish Time |
|---|---|---|---|---|
| CelebA | 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image. | Download | attribute & landmark | 2015 |
| IMDB-WIKI | 500k+ face images with age and gender labels | Download | age & gender | 2015 |
| Adience | Unfiltered faces for gender and age classification | Download | age & gender | 2014 |
| WFLW? | WFLW contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks. | Download | landmarks | 2018 |
| Caltech10k Web Faces | The dataset has 10,524 human faces of various resolutions and in different settings | Download | landmarks | 2005 |
| EmotioNet | The EmotioNet database includes950,000 images with annotated AUs. A subset of the images in the EmotioNet database correspond to basic and compound emotions. | Download | AU and Emotion | 2017 |
| RAF( Real-world Affective Faces) | 29672 number of real-world images, including 7 classes of basic emotions and 12 classes of compound emotions, 5 accurate landmark locations, 37 automatic landmark locations, race, age range and gender attributes annotations per image | Download | Emotions、landmark、race、age and gender | 2017 |
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迷若烟雨
领先的深度学习解决问题提供商
- 擅长领域：
- 深度学习
- 人脸识别