Frontal face generation from multiple low-resolution non-frontal faces for face recognition

Authors:
Yuki Kono;Tomokazu Takahashi;Daisuke Deguchi;Ichiro Ide;Hiroshi Murase
Affiliations:
Graduate School of Information Science, Nagoya Univesity, Nagoya, Japan;Graduate School of Information Science, Nagoya Univesity, Nagoya, Japan and Faculty of Economics and Information, Gifu Shotoku Gakuen University, Japan;Graduate School of Information Science, Nagoya Univesity, Nagoya, Japan;Graduate School of Information Science, Nagoya Univesity, Nagoya, Japan;Graduate School of Information Science, Nagoya Univesity, Nagoya, Japan
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Year:
2010

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Abstract

We propose a method of frontal face generation from multiple low-resolution non-frontal faces for face recognition. The proposed method achieves an image-based face pose transformation by using the information obtained from multiple input face images without considering three-dimensional face structure. To achieve this, we employ a patchwise image transformation strategy that calculates small image patches in the output frontal face from patches in the multiple input nonfrontal faces by using a face image dataset. The dataset contains faces of a large number of individuals other than the input one. Using frontal face images actually transformed from low-resolution non-frontal face images, two kinds of experiments were conducted. The experimental results demonstrates that increasing the number of input images improves the RMSEs and the recognition rates for low-resolution face images.