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Most facial emotion recognition algorithms assume that the face is near frontal and the face pose fixed during the recognition process. However, such constrain limits the adoption for real-world applications. To solve this, pose-invariant descriptor for emotion recognition is required. This work proposes a novel pose-invariant dynamic descriptor that encodes the relative movement information of facial landmarks. The proposed feature set is able to handle speed variations and continuous head pose variations, while the subject is expressing an emotion. In addition, the proposed method is fast and thus can be realize real-time implementation for real-world application. Performance evaluation done using three publicly available databases; Cohn-Kanade $$(\hbox {CK}^{+})$$ , Amsterdam Dynamic Facial Expression Set (ADFES), and Audio Visual Emotion Challenge (AVEC 2011) showed that our proposed method outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Humans process faces to recognize family resemblance and act accordingly. Undoubtedly, they are capable of recognizing their kin and family members. In this paper, we study the facts and valid assumptions of facial resemblance in family members' facial segments. Our analysis and psychological studies show that the facial resemblance differs from member to member and depends on image segments. First, we estimate the degree of resemblance of each member's image segment. Then, we propose a novel method to fuse similarity of each member's facial image segments to perform family verification. Employing the proposed approach on the collected 5,400-sample family database achieves considerable improvement compared to the state-of-the-art fusion rule in three designated test scenarios. Experimental results also show that the proposed approach could estimate the similarity slightly more accurate than human perception. We believe the public availableness of the database may advance the development in this domain. [ABSTRACT FROM AUTHOR]

There are still many challenging problems in facial gender recognition which is mainly due to the complex variances of face appearance. Although there has been tremendous research effort to develop robust gender recognition over the past decade, none has explicitly exploited the domain knowledge of the difference in appearance between male and female. Moustache contributes substantially to the facial appearance difference between male and female and could be a good feature to be incorporated into facial gender recognition. Little work on moustache segmentation has been reported in the literature. In this paper, a novel real-time moustache detection method is proposed which combines face feature extraction, image decolorization and texture detection. Image decolorization, which converts a color image to grayscale, aims to enhance the color contrast while preserving the grayscale. On the other hand, moustache appearance is normally grayscale surrounded by the skin color face tissue. Hence, it is a fast and efficient way to segment the moustache by using the decolorization technology. In order to make the algorithm robust to the variances of illumination and head pose, an adaptive decolorization segmentation has been proposed in which both the segmentation threshold selection and the moustache region following are guided by some special regions defined by their geometric relationship with the salient facial features. Furthermore, a texture-based moustache classifier is developed to compensate the decolorization-based segmentation which could detect the darker skin or shadow around the mouth caused by the small lines or skin thicker from where he/she smiles as moustache. The face is verified as the face containing a moustache only when it satisfies: (1) a larger moustache region can be found by applying the decolorization segmentation; (2) the segmented moustache region is detected as moustache by the texture moustache detector. The experimental results on color FERET database showed that the proposed approach can achieve 89 % moustache face detection rate with 0.1 % false acceptance rate. By incorporating the moustache detector into a facial gender recognition system, the gender recognition accuracy on a large database has been improved from 91 to 93.5 %. [ABSTRACT FROM AUTHOR]