Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

Abstract

Abstract-The main weakness of the k-Nearest Neighbor algorithm in face recognition is calculating the distance and sort all training data on each prediction which can be slow if there are a large number of training instances. This problem can be solved by utilizing the priority k-d tree search to speed up the process of k-NN classification. This paper proposes a method for student attendance systems in the classroom using facial recognition techniques by combining three levels of Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA) to extract facial features followed by applying the priority of k-d tree search to speed up the process of facial classification using kNearest Neighbor. The proposed algorithm is tested on two datasets that are Honda/UCSD video dataset and our dataset (AtmafaceDB dataset). This research looks for the best value of k to get the right facial recognition using k-fold cross-validation. 10-fold cross-validation at level 3 DWT-PCA shows that face recognition using k-Nearest Neighbor on our dataset is 95.56% with k = 5, whereas in the Honda / UCSD dataset it is only 82% with k = 3. The proposed method gives computational recognition time on our dataset 40 milliseconds.

Generation of high dynamic range for enhancing the panorama environment

“…After examine the leaf node, the top entry in the priority queue is removing and the next search is beginning from the sub-tree that contains the next feature descriptor. The search is continuing until the queue is empty of the query number exceed qmax (qmax represent maximum query number in this paper [23][24][25]. c. Homography using RANSAC In this phase, the useful information that obtained from features matching phase is use to do image matching.…”