Abstract
This paper presents a new method for face verification for vision applications. There are many approaches to detect and track a face in a sequence of images; however, the high computations of image algorithms, as well as, face detection and head tracking failures under unrestricted environments remain to be a difficult problem. We present a robust algorithm that improves face detection and tracking in video sequences by using geometrical facial information and a recurrent neural network verifier. Two types of neural networks are proposed for face detection verification. A new method, a three-face reference model (TFRM), and its advantages, such as, allowing for a better match for face verification, will be discussed in this paper.
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Yoon, S.H., Hur, G.T., Kim, J.H. (2006). Recurrent Neural Network Verifier for Face Detection and Tracking. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_53
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DOI: https://doi.org/10.1007/11779568_53
Publisher Name: Springer, Berlin, Heidelberg
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