Abstract
In the recent era facial image processing is gaining more importance and the face detection from image or from video have number of applications which are video surveillance, entertainment, security, multimedia, communication, Ubiquitous computing etc. Various research work are carried out for face detection and processing which includes detection, tracking of the face, estimation of pose, clustering the detected faces etc. Although significant advances have been made, the performance of face detection systems provide satisfactory under controlled environment & may get degraded with some challenging scenario such as in real time video face detection and processing. There are many real-time applications where human face serves as identity and these application are time bound so time for detection of face from image or video and the further processing is very essential, thus here our goal is to discuss the face detection system overview and to review various human skin colors based approaches and Haar feature based approach for better detection performance. Detected faces tagging and clustering is essential in some cases, so for such further processing time factor plays important role. Some of the recent approaches to improve detection speed such as using Graphical Processing Unit are discussed and providing future directions in this area.
Highlights
Nowadays human face detection is very essential
In this process facial features plays vital role,which are stored in database; if the features specified in criteria are matched face detection can be successful otherwise it invalidate the matching process
While detecting frontal faces the facial features like eyes, nose mouth are visible in the image
Summary
Nowadays human face detection is very essential. It is gaining importance in various applications. A. Pose: Multi view face detection is very essential, but pose variation due to angular movement of face towards left and right side cause the failure of detection of different features like nose, eyes, eye brows & ears. Pose: Multi view face detection is very essential, but pose variation due to angular movement of face towards left and right side cause the failure of detection of different features like nose, eyes, eye brows & ears These features can be partially occluded and the process of face detection may fail. Developed CPUs have multiple cores processing in parallel manner whereas, GPU uses a parallel architecture which has many cores i.e. smaller processing elements They can perform a very high degree of data based parallelism.
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