This paper presents a local feature-based method for matching facial sketch images to face photographs, which
is the first known feature-based method for performing such matching. Starting with a training set of sketch to
photo correspondences (i.e. a set of sketch and photo images of the same subjects), we demonstrate the ability
to match sketches to photos: (1) directly using SIFT feature descriptors, (2) in a "common representation" that
measures the similarity between a sketch and photo by their distance from the training set of sketch/photo pairs,
and (3) by fusing the previous two methods. For both matching methods, the first step is to sample SIFT feature
descriptors uniformly across all the sketch and photo images. In direct matching, we simply measure the distance
of the SIFT descriptors between sketches and photos. In common representation matching, the distance between
the descriptor vectors of the probe sketches and gallery photos at each local sample point is measured. This
results in a vector of distances across the sketch or photo image to each member of the training basis. Further
recognition improvements are shown by score level fusion of the two sketch matchers. Compared with published
sketch to photo matching algorithms, experimental results demonstrate improved matching performances using
the presented feature-based methods.
KEYWORDS: Facial recognition systems, Video surveillance, Video, Video compression, Surveillance, Image compression, Personal digital assistants, Cameras, Detection and tracking algorithms, Mobile devices
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.
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