A multi-task framework for facial attributes classification through end-to-end face parsing and deep convolutional neural networks - Khan, Khalil; Attique, Muhammad; Khan, Rehan Ullah; Syed, Ikram; Chung, Tae Sun (Sensors (Switzerland), 2020)
Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.
Keywords: Convolutional neural network; Extracting features; Face image analysis; Face parsing; Feature descriptors; Gender recognition; Learning-based segmentation; Probabilistic classification method; Age Factors; Algorithms; Continental Population Groups; Databases as Topic; Deep Learning; Face; Female; Humans; Image Processing, Computer-Assisted; Male; Neural Networks, Computer; Deep learning; Face image analysis; Face parsing; Facial attributes classification