VEGAC: Visual Saliency-based Age, Gender, and Facial Expression Classification Using Convolutional Neural Networks
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This paper explores the use of Visual Saliency to Classify Age, Gender and Facial Expression for Facial Images. For multi-task classification, we propose our method VEGAC, which is based on Visual Saliency. Using the Deep Multi-level Network [1] and off-the-shelf face detector [2], our proposed method first detects the face in the test image and extracts the CNN predictions on the cropped face. The CNN of VEGAC were fine-tuned on the collected dataset from different benchmarks. Our convolutional neural network (CNN) uses the VGG-16 architecture [3] and is pre-trained on ImageNet for image classification. We demonstrate the usefulness of our method for Age Estimation, Gender Classification, and Facial Expression Classification. We show that we obtain the competitive result with our method on selected benchmarks. All our models and code will be publically available.
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