Dataset of Large Gathering Images for Person Identification and Tracking

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi Arabia.This dataset consists of raw and processed images reflecting a highly challenging and unconstraint environment.The methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face regions.The raw images in the dataset consist of a total of 4613 frames obtained fromvideo sequences.The processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented data.The dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 images.It portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research purposes.We have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal context.This can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

Purpose-In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination,background,occlusion and other factors,we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern(CS-LBP)and deep residual network(DRN)model.Design/methodology/approach-The algorithm first extracts the block CSP-LBP features of the face image,then incorporates the extracted features into the DRN model,and gives the face recognition results by using a well-trained DRN model.The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.Findings-Compared with the direct usage of the original image,the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency.Experimental results on the face datasets of FERET,YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.Originality/value-The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment,and it is particularly robust to the change of illumination,which proves its superiority.