Abstract:Automated detection of sensor level spoof attacks is critical for success of surveillance technologies and the detection of crimes. This paper presents a new approach to more accurately detect such spoof face presentation attempts during the surveillance. The image data from the spoof samples not only illustrates the subtle texture difference in the spatial domain but is also accompanied by temporal differences as compared to those from the live/real human samples. Therefore, we investigate a new approach to e… Show more
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