Face detection for video summary using enhancement-based fusion strategy under varying illumination conditions

Abstract

A biometric-based techniques emerge as the promising approach for most of the real-time applications including security systems, video surveillances, human-computer interaction and many more. Among all biometric methods, face recognition offers more benefits as compared to others. Diagnosing human faces and localizing them in images or videos is the priori step of tracking and recognizing. But the performance of face detection is limited by certain factors namely lighting conditions, pose variation, occlusions, low resolution images and complex background. To overcome the problems, this paper examines a fusion strategy in the enhancement-based skincolor segmentation approach that can improve the performance of face detection algorithm. The method is robust against complex background, ethnicity and lighting variations. The method consists of three steps. The first step receives spatial transform techniques in parallel to enhance the contrast of the image, change the color space of the enhanced images to YCbCr, apply skin segmentation technique and yield the binary segmented images. The second step ascertains the weight of accuracy (WoA) of each of the segmented image and fed it into the fusion strategy to get the final skin detected region. Finally, the last step localizes the human face. The methodology is not constrained to just frontal face identification. However it is invariant with the diverse head postures, enlightment condition, and size of faces. The experimental result demonstrates the improvement in the accuracy and precision along with the reduction in FPR as compared to other enhancement classifiers.

Face detection for video summary using enhancement-based fusion strategy under varying illumination conditions

“…The weight for each classifier is computed using (7). where, k = 1 to 5, FPR i and FNR i represents the false positive rate and false negative rate of i th classifier, respectively and are calculated using (8) and (9). The manually labeled ground truth of the image constructed in preprocessing phase has been used for estimating the FPR and FNR of each classifier.…”

“…Finally, the segmented components are labelled, their areas and centroids are calculated, and the bounding boxes are drawn around the detected human faces. This paper is the extended work of [9] on different datasets. The rest of the paper is organized as follows: Section 2 discusses the related work.…”

A Computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset for Differently Abled People Using Artificial Intelligence

“…Te perceivable gesture happens within a set duration that the performer physically orchestrates during static motions. Te fnger and hand positions are recognized and examined one after the other [8]. Other sign languages, such as British Sign Language (BSL), American Sign Language (ASL), Arabic Sign Language (ArSL), and Spanish Sign Language (SSL), are used in diferent regions of the globe [9].…”