Video-Based Face Recognition Using a Metric of Average Euclidean Distance
نویسندگان
چکیده
This paper presents a novel approach for video-based face recognition. We define a metric based on an average 2 L Euclidean distance between two videos as the classifier. This metric makes use of Earth Mover’s Distance (EMD) as the underlying similarity measurement between videos. Earth Mover’s Distance is a recently proposed metric for geometric pattern matching and it reflects the average ground distance between two distributions. Under the framework of EMD, each video is modeled as a video signature and Euclidean distance is selected as the ground distance of EMD. Since clustering algorithm is employed, video signature can well represent the overall data distribution of faces in video. Experimental results demonstrate the superior performance of our algorithm.
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