Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

Gender Classification from Facial Images

Gender classification based on facial images has received increased attention in the computer vision community. In this work, a comprehensive evaluation of state-of-the-art gender classification methods is carried out on publicly available databases and extended to reallife face images, where face detection and face normalization are essential for the success of the system. Next, the possibility of predicting gender from face images acquired in the near-infrared spectrum (NIR) is explored. In this regard, the following two questions are addressed: (a) Can gender be predicted from NIR face images; and (b) Can a gender predictor learned using visible (VIS) images operate successfully on NIR images and vice-versa? The experimental results suggest that NIR face images do have some discriminatory information pertaining to gender, although the degree of discrimination is noticeably lower than that of VIS images. Further, the use of an illumination normalization routine may be essential for facilitating cross-spectral gender prediction. By formulating the problem of gender classification in the framework of both visible and near-infrared images, the guidelines for performing gender classification in a real-world scenario is provided, along with the strengths and weaknesses of each methodology. Finally, the general problem of attribute classification is addressed, where features such as expression, age and ethnicity are derived from a face image