Performance evaluation study of data selection and matching criteria in face recognition for human surveillance

Ervin Gubin Moung (2018) Performance evaluation study of data selection and matching criteria in face recognition for human surveillance. Doctoral thesis, Universiti Malaysia Sabah.

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Face recognition is an important biometric in many fields, such as access control and surveillance. Currently there are many published reports in face recognition for surveillance application. However, there was no standard surveillance database, no standard evaluation criteria, and no standard method published for selecting training and testing data used by all researchers. Thus, a comparison between the various results reported is hard to be made. The aim of this work is to establish a standard training database selection method based on surveillance database suitable for face recognition for surveillance application and also to establish. a test bed and test criteria for evaluating common reported approach. Two surveillance databases are used; ChokePoint Pre-processed Grayscale Database (PPG) and ChokePoint Manually Preprocessed Colour Images Database (MPCI). Three sessions are used to acquire the images in the database. The images in each session were equally divided into three classes: CLOSE, MEDIUM, and FAR. A commonly used PCA-based face recognition system has been selected for this work. The effect of the distance between the subject and the camera, the effect of the number of images per class, the effect of mean image, the effect of training database size, and the effect of database sessions on face recognition have been investigated. It was found that using images from the FAR class for training gives better performance compared to MEDIUM or CLOSE class. However, it was found that using one image from each class gives better recognition performance compared to using three FAR class images for training. It was also found that as the number of images per class increases, the recognition rate increases. Finally, it was found that using one mean image per class from all the available database sessions gives the best performance. The performance of YCsCR individual channels on face recognition has been investigated and compared with grayscale. It was found that grayscale performed better than all the individual YCsCR channels because grayscale has better quality of visual features. A fusion strategy using the individual YCsCR has been presented and compared to grayscale performance. It was found out that the fusion of CsxCR with any other channel outperforms the grayscale when three images of the same class from the same database are used for training (Case 1). For YCBCR individual channels, the best performance is achieved by using the CsxCR channel. It was also found that if the differences between individual channels performance vary significantly, the individual channel performance become an important criteria when selecting channels for fusion. In general, increasing the number of fused channels increases the performance of the system. A comparison of PPG and MPCI databases performance and also a comparison of Euclidean distance (ED) and Euclidean distance with SVM (ED+SVM) performance have been made. It was found that the recognition rate pattern stays the same regardless of the training database used and similarity matching method used. Processing time wise, ED is much more efficient compared to ED+SVM. The best recognition performance is achieved by PCA-based (ED) system using the MPCI database and ED matching method with Case 6 training database criteria, giving 100% average correct recall and reject rate, and uses 5.69 seconds process time for a single test person and 26. 7 MB space for training data.

Item Type: Thesis (Doctoral)
Keyword: Face recognition, surveillance application, Human surveillance
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Department: FACULTY > Faculty of Engineering
Depositing User: DG MASNIAH AHMAD -
Date Deposited: 29 Nov 2023 10:21
Last Modified: 29 Nov 2023 10:21

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