A Comparison of the YCBCR Color Space with Gray Scale for Face Recognition for Surveillance Applications

Abstract

Face recognition is an important biometric method because of its potential applications in many fields, such as access control and surveillance. In this paper, the performance of the individual channels from the YCBCR color space on face recognition for surveillance applications is investigated and compared with the performance of the gray scale. In addition, the performance of fusing two or more color channels is also compared with that of the gray scale. Three cases with different number of training images per persons were used as a test bed. It was found out that, the gray scale always outperforms the individual channel. However, the fusion of CBxCR with any other channel outperforms the gray scale when three images of the same class from the same database are used for training. Regardless of the cases used, the CBxCR channel always gave the best performance for the individual color channels. It was found that, in general, increasing the number of fused channels increases the performance of the system. It was also found that the gray scale channel is the better choice for face recognition since it contain better quality edges and visual features which are essential for face recognition.

GECA: A Global Edge Computing Architecture

“…The Internet of Things, has been defined by various authors as the interaction and communication carried out between the connected devices that generate and exchange data with things or objects of the real world [2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

An Energy-Aware Dynamic Resource Management Technique Using Deep Q-Learning Algorithm and Joint VM and Container Consolidation Approach for Green Computing in Cloud Data Centers

“…Consolidation is one of the most significant solutions for dynamic resource management in cloud data centers, which take advantage of virtualization technology [11][12][13][14][15]. Consolidation occurs by live migration VMs and containers in cloud data centers.…”

A deep tech architecture for intelligent IoT systems

“…As can be seen in the name of Distributed Artificial Intelligence (DAI), the advances in both distributed systems and artificial intelligence are the main fields of The role of Artificial Intelligence and distributed computing in IoT applications, pp. 41-54 [43] Mehmet Ozturk / A deep tech architecture for intelligent IoT systems driving. Edge Computing provides an excellent way to use autonomous software or hardware intelligence agents in a distributed manner [39][40][41][42][43][44][45][46][47].…”

“…41-54 [43] Mehmet Ozturk / A deep tech architecture for intelligent IoT systems driving. Edge Computing provides an excellent way to use autonomous software or hardware intelligence agents in a distributed manner [39][40][41][42][43][44][45][46][47]. This comes with a question: How far will artificial intelligence eventually go?…”