Assessment of H.264 video compression on automated face recognition performance in surveillance and mobile video scenarios

Abstract

We assess the impact of the H.264 video codec on the match performance of automated face recognition in surveillance and mobile video applications. A set of two hundred access control (90 pixel inter-pupilary distance) and distance surveillance (45 pixel inter-pupilary distance) videos taken under non-ideal imaging and facial recognition (e.g., pose, illumination, and expression) conditions were matched using two commercial face recognition engines in the studies. The first study evaluated automated face recognition performance on access control and distance surveillance videos at CIF and VGA resolutions using the H.264 baseline profile at nine bitrates rates ranging from 8kbs to 2048kbs. In our experiments, video signals were able to be compressed up to 128kbs before a significant drop face recognition performance occurred. The second study evaluated automated face recognition on mobile devices at QCIF, iPhone, and Android resolutions for each of the H.264 PDA profiles. Rank one match performance, cumulative match scores, and failure to enroll rates are reported.

Impact of Three-Dimensional Video Scalability on Multi-View Activity Recognition using Deep Learning

“…In general, videos can have various combinations of spatial, temporal, and quality resolutions, which is noted as video scalability. Previous studies in several fields investigated how it affects the performance of the video systems, including surveillance [11,14], face recognition [2,11], visual perception of users [23,13,4], and so on. The video scalability has been also considered in human activity recognition tasks.…”

Análisis comparativo de prestaciones de tráfico de video multicast en redes heterógeneas

“…_____________________________________________________________ 16 Higinio Alberto Facchini Análisis Comparativo de Prestaciones de Tráfico de Video multicast en Redes Heterogéneas…”

“…En una línea de investigación totalmente diferente [16] se evaluó el impacto de los codec de vídeo H.264 en el rendimiento de coincidencia para el reconocimiento facial automatizado de vigilancia y aplicaciones de vídeo móvil. En los estudios se aparearon un conjunto de doscientos videos de control de acceso y de vigilancia a distancia tomados bajo condiciones de imagen y reconocimiento facial no ideales mediante dos motores de reconocimiento facial comercial.…”

“…Hay distintos trabajos sobre las temáticas consideradas, como el análisis de tráfico multicast y protocolos de ruteo multicast [3][4][5][6], análisis de codecs de video [7][8][9][10][11][12], análisis de tráfico de video bajo distintos contextos particulares [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], análisis de tráfico de video bajo multicast [29][30][31], análisis comparativo de multicast vs unicast [32], análisis de QoS para tráfico de video [33] y trabajos sobre IPTV [34][35][36]. En este trabajo se planteó obtener conclusiones cuantitativas desde estudios experimentales, sobre las mejores opciones de tráfico de video en redes de laboratorio WAN usando equipamiento real.…”

Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

“…Other variables such as the quality of an image (e.g., ratio, resolution, compression, blur), time lapse or facial aging, as well as occlusion contribute to face recognition errors [31,32]. During the assessment of face recognition within videos, issues such as segmenting the face in varying illuminations [33] and compression artifacts [34] must be considered. One of the most challenging tasks in face recognition is the comparison of two images that have been captured using alternate sensors or imaging modalities (e.g.…”