PhD Student, University of KwaZulu-Natal
Enhancement of security using face detection software
Security is a concern worldwide. To this end, researchers have devoted much time to develop systems to aid in tackling this challenge. The proposed system employs a combination of algorithms to detect intruders using special characteristics of the human face. It also intelligently detects the age of the intruder and captures the emotional composure of the intruder.
The research has three stages such as: (a) face detection, (b) feature extraction and (c) facial expression recognition. The first phase i.e. face detection involves skin color detection using RGB (Red, green and Blue) color model, lighting compensation for getting the human face, and also for retaining the required face portion. The output of the first phase captures the facial features like eyes, nose, and mouth using AAM (Active Appearance Model) method.
The second phase has to do with feature extraction. This involves the extraction of special facial features that are unique to individuals. It includes the dimension of the eye-socket measuring from the nose portion to the other extreme, the mouth positioning, and the length and breadth of the nose.
The third phase detects the emotional composure of the individual using the mouth and the eyes.
Abstract: An Unmanned Ariel vehicle (UAV) has greater importance in the army for border security. The main objective of this article is to develop an OpenCV-Python code using Haar Cascade algorithm for object and face detection. Currently, UAVs are used for detecting and attacking the infiltrated ground targets. The main drawback for this type of UAVs is that sometimes the object are not properly detected, which thereby causes the object to hit the UAV. This project aims to avoid such unwanted collisions and damages of UAV. UAV is also used for surveillance that uses Voila-jones algorithm to detect and track humans. This algorithm uses cascade object detector function and vision. train function to train the algorithm. The main advantage of this code is the reduced processing time. The Python code was tested with the help of available database of video and image, the output was verified.
Pub.: 23 Nov '16, Pinned: 17 Nov '17
Abstract: Face detection and recognition has been prevalent with research scholars and diverse approaches have been incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images and video image to be used for detection and recognition. This led to newer methods for face detection and recognition to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA), have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize nonlinear faces with an acceptance ratio of more than 90% and execution time of only few seconds.
Pub.: 28 Jan '17, Pinned: 17 Nov '17
Abstract: With increasing crime rates in today’s world, there is a corresponding awareness for the necessity of detecting abnormal activity. Automation of abnormal Human behavior analysis can play a significant role in security by decreasing the time taken to thwart unwanted events and picking them up during the suspicion stage itself. With advances in technology, surveillance systems can become more automated than manual. Human Behavior Analysis although crucial, is highly challenging. Tracking and recognizing objects and human motion from surveillance videos, followed by automatic summarization of its content has become a hot topic of research. Many researchers have contributed to the field of automated video surveillance through detection, classification and tracking algorithms. Earlier research work is insufficient for comprehensive analysis of human behavior. With the introduction of semantics, the context of a surveillance domain may be established. Such semantics may extend surveillance systems to perform event-based behavior analysis relevant to the domain. This paper presents a survey on research on human behavior analysis with a scope of analyzing the capabilities of the state-of-art methodologies with special focus on semantically enhanced analysis.
Pub.: 29 Apr '12, Pinned: 17 Nov '17
Abstract: Ambient Intelligent applications involve the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs (Ambient Intelligence-AmI). Biometrics plays a key role in surveillance and security applications. Fingerprint, iris and voice/speech traits can be acquired by contact, contact-less, and at-a-distance sensors embedded in the environment. Biometric traits transmission and delivery is very critical and it needs real-time transmission network with guaranteed performance and QoS. Wireless networks become suitable for AmI if they are able to satisfy real-time communication and security system requirements. In this paper an hierarchical network architecture, made up of several independent Wireless Automation Cells grouped in Automation Clusters, is presented. The performance evaluation of the proposed architecture, in terms of authentication accuracy and network scheduling efficiency, is also outlined.
Pub.: 11 Jan '12, Pinned: 17 Nov '17
Abstract: Security methods based on biometrics have been gaining importance increasingly in the last few years due to recent advances in biometrics technology and its reliability and efficiency in real world applications. Also, several major security disasters that occurred in the last decade have given a new momentum to this research area. The successful development of biometric security applications cannot only minimise such threats but may also help in preventing them from happening on a global scale. Biometric security methods take into account humans’ unique physical or behavioural traits that help to identify them based on their intrinsic characteristics. However, there are a number of issues related to biometric security, in particular with regard to the poor visibility of the images produced by surveillance cameras that need to be addressed. In this paper, we address this issue by proposing an integrated image enhancement approach for face detection. The proposed approach is based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It can adjust the colour cast and maintain the luminance of the whole image at the same level. We evaluate the performance of the proposed approach by applying three face detection methods (skin colour based face detection, feature based face detection and image based face detection) to surveillance images before and after enhancement using the proposed approach. The results show a significant improvement in face detection when the proposed approach was applied.
Pub.: 30 May '12, Pinned: 17 Nov '17