Statistics of the number of students in the classroom is very important for class surveillance. It can help teacher count the
number of students and help students choose class for self-study. While as a canonical pattern recognition problem, it’s
very difficult due to various appearances of students and other outliers such as bags and books. We want to find a good
solution to this problem. A novel method for texture feature extraction is now proposed based on that difference of
Frequency spectrum image belongs to different seat image. Regarding frequency spectrum image as the texture image,
the texture characteristics which can represent those differences are extracted using texture analysis's method. At the
same time, we combine the Local binary patterns feature with the texture characteristics to describe the texture of seats.
Experiments on a real classroom dataset demonstrate that the accuracy of the proposed method reaches 91.3%.
Face recognition under changing lighting conditions and facial expression are a challenging problem in computer vision.
The variations in illumination and facial expressions can dramatically reduce the performance of face recognition. In this
paper, an efficient method for face recognition which is robust under illumination and facial expressions variations. The
core of the algorithm based on dense correspondence which we used is characterized by LBP and regional gradient
between images. Our experiment on the AR databases and ORL face databases, ORL databases as a supplement in this
framework. The results show that the proposed approach is not only efficient but also outperforms the comparative