Structured Output SVM Prediction of Apparent Age, Gender and Smile From Deep Features

Michal Uricar, Radu Timofte, Rasmus Rothe, Jiri Matas, Luc Van Gool; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 25-33

We propose structured output SVM for predicting the apparent age as well as gender and smile from a single face image represented by deep features. We pose the problem of apparent age estimation as an instance of the multi-class structured output SVM classifier followed by a softmax expected value refinement. The gender and smile are treated as binary classification problems. The proposed solution first detects the face in the image and then extracts deep features from the cropped image around the detected face. We use a convolutional neural network with VGG-16 architecture for learning deep features. The network is pretrained on the ImageNet database and then fine-tunned on IMDB-WIKI and ChaLearn 2015 LAP datasets. We validate our methods on the ChaLearn 2016 LAP dataset. Our structured output SVMs are trained solely on ChaLearn 2016 LAP data. We achieve excellent results for both apparent age prediction and gender and smile classification.