(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, 2017 Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA Hadi Santoso Agus Harjoko, Agfianto Eko Putra Department of Computer Science and Electronics, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Science Faculty of Mathematics and Natural Science Universitas Gadjah Mada Universitas Gadjah Mada Yogyakarta, Indonesia Yogyakarta, Information System Study Program Indonesia STMIK Atma Luhur Pangkalpinang, Indonesia Abstract—The main weakness of the k-Nearest Neighbor determine whether each student is present or absent and will be algorithm in face recognition is calculating the distance and sort recognized in the group of courses followed. all training data on each prediction which can be slow if there are a large number of training instances. This problem can be solved The contibutions of this paper include: 1) combining three by utilizing the priority k-d tree search to speed up the process of levels of 2D DWT-PCA for feature extraction; 2) we adapted k-NN classification. This paper proposes a method for student approximate nearest neighbors search algorithm using the attendance systems in the classroom using facial recognition priority of k-d tree to find some (k) nearest neighbors from a techniques by combining three levels of Discrete Wavelet certain query point (q) efficiently [2] Transforms (DWT) and Principal Component Analysis (PCA) to extract facial features followed by applying the priority of k-d The paper is structured as follows: Section 2 consists of tree search to speed up the process of facial classification using k- related research discussions, Section 3 consists of the proposed Nearest Neighbor. The proposed algorithm is tested on two research method, Section 4 presents the results of research and datasets that are Honda/UCSD video dataset and our dataset analysis, and Section 5 offers some conclusions. (AtmafaceDB dataset). This research looks for the best value of k to get the right facial recognition using k-fold cross-validation. II. RELATED WORKS 10-fold cross-validation at level 3 DWT-PCA shows that face Human facial recognition plays an important role in recognition using k-Nearest Neighbor on our dataset is 95.56% biometrics. The eigenvector-based method of facial recognition with k = 5, whereas in the Honda / UCSD dataset it is only 82% was first introduced by [3], and then expanded upon by [4] and with k = 3. The proposed method gives computational recognition [5]. The eigenvector-based method extracts the low- time on our dataset 40 milliseconds. dimensional subspace, which tends to simplify the Keywords—Multiple-face recognition; DWT; PCA; priority k-d classification process. Chitaliya and Trivedi [6] developed a tree; k-Nearest Neighbor facial recognition model that used wavelet-PCA for feature extraction, then used Euclidean distance and neural networks I. INTRODUCTION for classification. In this study, Level 1 discrete wavelet transform (DWT) was used. They were able to attain up to Facial recognition performance is influenced by several 93.3% accuracy. variables, including pose, expression, lighting, and occlusion (namely, glasses, mustaches, beards, headgear, etc.) [1]. In 2012, Rao [7] proposed a facial recognition system using discrete wavelet transform (DWT) and eigenvectors, showing Many educational institutions in Indonesia continue to use an average of 3.25% improvement in recognition performance. attendance sheets, allowing students to cheat by asking friends to sign their names. The use of attendance sheets has proven to Research on attendance management in the classroom be time-consuming, unreliable, inaccurate, and inefficient. proposed by [8], using the haar cascade method for face Based on this issue, we propose a combination of face detection. While the face recognition using eigenface method. recognition methods using three levels of Discrete Wavelet This face recognition approach achieves an accuracy of 85%. Transforms (DWT) and Principal Component Analysis (PCA) to extract facial features followed by applying k-d tree to Research on improving facial recognition was proposed by accelerate the process of facial classification using k-NN. The [9], who used a wavelet-PCA decomposition method with k-NN process with Euclidean distance is used to identify mahalanobis classification. The results of this study improved students’ presence through pictures or video of a student’s face. recognition by 95.7%. Recognition results using Euclidean By using this approach, it is possible that automatically classification reached 93.8% accuracy, with a computing speed integrated systems between IP camera and pc or notebooks will of 8.501 milliseconds. 112 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, 2017 In 2014, Mao [10] conducted research into multiple face efficiently and search for the location of the nearest neighbors. detection, tracking, and recognition in the classroom using a K-d tree data structure has been used as a data structure for Honda/UCSD dataset of videos. In the experiment, the student overcoming increased processing times caused by the addition dataset consisted of 16 and 39 people in the classroom. Fifty- of features into the database. An alternative approach is nine videos were used: 20 for training and 39 for testing. The establishing a balanced k-d tree, as proposed by [22]. The study used the Haar cascade facial detection method. For facial nearest neighbor search algorithm using the k-d tree data recognition, this study used eigenfaces, LBP+K-mean. The structure can be found in [23]. In their research, they only average precision and recall values in this study were more found one nearest neighbor. We integrate the priority k–d tree than 90%. search and best bin first method (BBF) to find the nearest neighbors, and the Euclidean distance is utilized as similarity The combination of methods for student attendance measure. systems in the classroom was proposed by [11] using facial recognition techniques by incorporating Discrete Wavelet In our study, the nearest proposed neighbor search Transforms (DWT) and Discrete Cosine Transform (DCT) to algorithm uses a data structure called a priority queue that extract the facial features of students followed by applying stores the list of closest neighbors k with some distance to the Radial Basis Function (RBF) to classify face objects. The query point q. The priority queue has a fixed upper limit on the success rate of the proposed system in recognizing facial number of elements (or points) that can be stored, which is the images of students who sit in the classroom is about 82%. number of nearest neighbors k. Each time a new element is added to the queue, if the queue is at a predetermined capacity, In 2017, Sayeed [12] presented an automated attendance the element with the highest priority value (the longest monitoring system with face recognition in a real-time distance) is removed from the queue. background world for with a database of student’s information by using Principal Component Analysis (PCA) algorithm. The III. PROPOSED METHOD testing results have been tested and taken from few different environment backgrounds. Basically is during the day and In order to make the framework, the proposed method in night time with lights either on or off. Average successful rate this study used was the method from related research and then of the proposed system are about 2.43 to 2.81. we developed them to be tested on our dataset. The research method used in this article consisted of several stages, as K-nearest neighbor (k-NN) is a simple and effective shown in Fig. 1. classification method. Samet [13] proposed a k-nearest neighbor algorithm using MaxNearestDist, while the k-NN A. Face Database Training Stage repair method had been proposed by [14]-[16]. The weakness 1) Single Face Image of k-NN algorithm, namely the process of calculating the In this stage, facial images of students were captured using similarity to be done on all existing training data [17]. If a digital camera. Facial data from 1,014 individuals was training data increases in number, the time for classification collected; each individual had nine images taken from different will also increase proportionately. The problem can be solved angles. A set of student facial images as training data is if using k-d tree data structure [18]-[21]. It can be used with a presented in Fig. 2. k-nearest neighbor (k-NN) approach to match facial features Face Database Training Stage 2D Discrete Single Face Face database Preprocessing Wavelet PCA Image with kd-tree Transform index Face Recognition Stage K-Nearest 2D Discrete Multiple Face Neighbor with Multiple Face Preprocessing Wavelet PCA Images / Video Euclidean Recognition Transform distance Fig. 1. The proposed method. Fig. 2. A set of student facial images as training data. 113 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, 2017 2) Preprocessing Discrete Wavelet Transform (DWT) is a discrete form of In the preprocessing stage, the following steps were the wavelet transform, consisting of a signal sampling process conducted: based on the scaling and shifting of parameters [25]. It is systematically defined by the following equation [26]: a) Facial cropping, the detection and localization of the face in a square area using the Viola-Jones method [24] j d j,k x(n)h(n 2 k) b) Changing the facial image from RGB to grayscale DWTx(n) (1) a x(n)g(n 2 j k) mode. j,k c) Resizing the facial image to 128x128 pixels. The coefficient refers to the detailed component of the d) Normalizing color brightness using a histogram signal ( ) and it is suited to the function of the wavelet, while equalization process.