Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 4; p. 712
• Main Authors: Liu, Yanpeng, Li, Yibin, Ma, Xin, Song, Rui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2017; MDPI
• Subjects: Algorithms; Databases, Factual; Face; Face recognition; Facial Expression; facial expression recognition; feature correction; Feature recognition; fusion features; hand-crafted features; Humans; Normalizing; Pattern recognition; Principal Component Analysis; Principal components analysis; salient facial areas; hand-crafted features; feature correction; salient facial areas; fusion features; facial expression recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17040712

In the pattern recognition domain, deep architectures are currently widely used and they have achieved fine results. However, these deep architectures make particular demands, especially in terms of their requirement for big datasets and GPU. Aiming to gain better results without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size; therefore, it can extracts more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features' dimensions are reduced by Principal Component Analysis (PCA) and we apply several classifiers to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames compared with neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. As a result, the salient areas found from different subjects are the same size. In addition, the gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.