Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier

Effective and Fast Face Recognition System Using Complementary OC-LBP and HOG Feature Descriptors With SVM Classifier

Geetika Singh (MCM DAV College for Women, Chandigarh, India) and Indu Chhabra (Department of Computer Science and Applications, Panjab University, Chandigarh, India)
Copyright: © 2018 |Pages: 20
DOI: 10.4018/JITR.2018010106
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Selection and implementation of a face descriptor that is both discriminative and computationally efficient is crucial. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) have been proven effective for face recognition. LBPs are fast to compute and are easy to extract the texture features. OC-LBP descriptors have been proposed to reduce the dimensionality of LBP while increasing the discrimination power. HOG features capture the edge features that are invariant to rotation and light. Owing to the fact that both texture and edge information is important for face representation, this article proposes a framework to combine OC-LBP and HOG. First, OC-LBP and HOG features are extracted, normalized and fused together. Next, classification is achieved using a histogram-based chi-square, square-chord and extended-canberra metrics and SVM with a normalized chi-square kernel. Experiments on three benchmark databases: ORL, Yale and FERET show that the proposed method is fast to compute and outperforms other similar state-of-the-art methods.
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Face recognition offers numerous applications including authentication, access control, surveillance and human computer interaction. It has been an active research area over the past decades, but is still a challenging problem as the human face can undergo wide variations such as those due to pose, illumination, age and occlusion. Among the three steps in face recognition – face segmentation, feature extraction and classification, feature extraction is considered crucial as it ultimately governs the recognition accuracy. The feature extraction algorithm needs to possess good representative and discriminative capability, should be computationally efficient and also robust to facial variations.

Feature extraction algorithms reported in the literature are either based on global approach or local approach. Global methods extract features from the whole face image and thus, deal with the complete facial information. Some popular global methods include Eigenfaces (Turk & Pentland, 1993; Martin, 2006; Yang, Zhang, Frangi, & Yang, 2004), Fisherfaces (Belhumeur, Hespanha, & Kriegman, 1996; Liu, Huang, Lu, & Ma, 2002; Li & Yuan, 2005), Independent Component Analysis (Bartlett, Movellan, & Sejnowski, 2002; Liu et al., 2005) and orthogonal rotation invariant moments (Singh, Mittal, & Walia, 2011; Singh, Walia, & Mittal, 2012; Haddadnia, Faez, Ahmadi, 2003; Pang, Teoh, & Ngo, 2006; Foon, Pang, Jin, & Ling, 2004; Arnold, Madasu, Boles, & Yarlagadda, 2007; Singh, Walia, & Mittal, 2011). These methods are widely used as they are simple, independent of any geometrical structures and easy to implement but exhibit two drawbacks – firstly, these are less comprehensive as they do not focus on precise details which is crucial for face representation and secondly, they are also affected by face variations. In contrast, local methods focus on image sub-regions and possess the capability to represent even the minute facial details. They also exhibit better invariance to light, pose and expression changes and thus, are being intensively explored for face identification. The widely used local face descriptors are Gabor Filters, Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and Scale Invariant Feature Transform (SIFT). Gabor Filters can efficiently represent the facial lines or edges and are invariant to scale and orientation (Struc, Gajsek, & Pavešić, 2009; Bhuiyan, & Liu, 2007). The main difficulty is, however, their high computational complexity. SIFT is another prominent descriptor in this category which can extract rotation invariant features but SIFT features are susceptible to light variations (Lowe, 2004; Soyel, & Demirel, 2010). In particular, LBP operator has proved to be the powerful and most successful descriptor and has been applied in numerous state-of-the-art face recognition systems (Ahonen, Hadid, & Pietikainen, 2004). It captures the texture information and represents local regions of the face efficiently by comparing each pixel with its neighboring pixels. The two most important benefits of LBP are its computational simplicity and its tolerance to monotonic illumination changes. However, the downside of the LBP descriptors is that the size of feature vector produced has high dimensionality. To address this issue, several variations of LBPs have been proposed in the literature which significantly reduce the dimensionality of the LBP feature vector (Huan, Shan, Ardebilian, Wang, & Chen, 2011). Recently, a variant of LBP, orthogonal combination of local binary patterns (OC-LBP) has been proposed for image description which generates less dimensional LBP features while still maintaining the discrimination power (Zhu, Biichot, & Chen, 2013). This method, in contrast to other LBP variations, has achieved accuracy improvement of up to 5% on standard texture classification datasets.

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