原文：https://blog.csdn.net/minstyrain/article/details/82257369
人臉屬性指的是根據給定的人臉判斷其性別、年齡和表情等，當前在github上開源了一些相關的工作，大部分都是基於tensorflow的，還有一部分是keras，CVPR2015曾有一篇是用caffe做的.
CSDN
從0到1實現基於Tornado和Tensorflow的人臉、年齡、性別識別
基於caffe的表情識別
tensorflow練習12：利用圖片預測年齡與性別
怎樣用Keras識別人物面部表情
github
https://github.com/GilLevi/AgeGenderDeepLearning:CVPR2015 caffe實現
https://github.com/dpressel/rude-carnie：CVPR2015對應的tensorflow實現
https://github.com/truongnmt/multi-task-learning: DEX: Deep EXpectation 實現
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同時預測性別和年齡，受限於使用的dlib檢測器，效果並不是很好
https://github.com/zZyan/race_gender_recognition:gender Accuracy: 0.951493,race Accuracy: 0.87557212
https://github.com/yu4u/age-gender-estimation:UTKFace庫 WideResNet,64*64輸入 keras模型，MAE 4.06
https://github.com/dandynaufaldi/Agendernetkeras實現了inception v3,mobilenet和ssr在imdb、utkface等的訓練
https://github.com/jocialiang/gender_classifier:性別識別全流程實現 94% accuracy
https://github.com/oarriaga/face_classification:表情識別
https://github.com/wondonghyeon/face-classification:性別和種族識別
https://github.com/shamangary/SSR-Net:年齡識別
https://github.com/b02901145/SSR-Net_megaage-asian：亞洲人優化
https://github.com/yu4u/age-gender-estimation：年齡和性別識別
https://github.com/isseu/emotion-recognition-neural-networks:表情66% with fer2013，性別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:表情識別
https://github.com/HectorAnadon/Face-expression-and-ethnic-recognition:表情和種族識別
https://github.com/ybch14/Facial-Expression-Recognition-ResNet66.7% on fer2013 with resnet50
https://github.com/JostineHo/mememoji:58% with 動畫展示
https://github.com/mangorocoro/racedetector:種族識別
https://github.com/HectorAnadon/Face-expression-and-ethnic-recognition:表情 72% accuracy ，種族95% accuracy
https://github.com/XiuweiHe/EmotionClassifier： 66% on fer2013 with mini_XCEPTION
https://github.com/truongnmt/multi-task-learning:多任務學習
useless
https://github.com/StevenKe8080/recognition_gender:使用爬取的圖片訓練
https://github.com/zonetrooper32/AgeEstimateAdience:
https://github.com/OValery16/gender-age-classification:
數據庫
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:標注了40個屬性，第21個屬性為性別
202,599 number of face images, and
5 landmark locations, 40 binary attributes annotations per image.
APPA-REAL ：視覺年齡估計，7,591張帶有實際年齡和視覺年齡標注的圖片，分為 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.暫未開放下載
FER+ :微軟重新標注的fer2013，表情識別比賽數據
NKI:GENKI數據集是由加利福尼亞大學的機器概念實驗室收集。該數據集包含GENKI-R2009a,GENKI-4K,GENKI-SZSL三個部分。GENKI-R2009a包含11159個圖像，GENKI-4K包含4000個圖像，分為“笑”和“不笑”兩種，每個圖片的人臉的尺度大小，姿勢，光照變化，頭的轉動等都不一樣，專門用於做笑臉識別。GENKI-SZSL包含3500個圖像，這些圖像包括廣泛的背景，光照條件，地理位置，個人身份和種族等
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