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In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set...
Facial landmark detection is a challenging task with broad applications. Many approaches have been proposed with varying degrees of success. Regression based methods update the facial point positions iteratively. The mean shape or shapes sampled from training set is often used as the initialization, which sometimes may lead to a local minimum in update due to the offset of initial positions and target...
This paper addresses the problem of transferring CNNs pre-trained for face recognition to a face attribute prediction task. To transfer an off-the-shelf CNN to a novel task, a typical solution is to fine-tune the network towards the novel task. As demonstrated in the state-of-the-art face attribute prediction approach, fine-tuning the high-level CNN hidden layer by using labeled attribute data leads...
Face recognition has became one of the most popular application in computer vision, due to a large demand for security. In recent years, major advances have been reported in the face recognition and, therefore, the methods are becoming more accurate and efficient. Recent research pointed that feature extraction using activity in directed graphs showed excellent results in feature extraction. In this...
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