Authors:
Masakazu Fujio
1
;
Keiichiro Nakazaki
2
;
Naoto Miura
2
;
Yosuke Kaga
1
and
Kenta Takahashi
1
Affiliations:
1
Research and Development Group Hitachi, Ltd., Yokohama, Kanagawa, Japan
;
2
Research and Development Group Hitachi, Ltd., Kokubunji, Tokyo, Japan
Keyword(s):
Bezier Curve, Semantic Segmentation, Shape-Aware Method, Finger Region Segmentationsed.
Abstract:
This paper presents a shape-aware finger region segmentation method from hand images for user authentication. The recent development of encoder-decoder network-based deep learning technologies dramatically improved image segmentation accuracy. Although those methods predict the probability of belonging to each object pixel by pixel, it is impossible to consider whether the estimated region has a finger-like shape. We adopted a deep learning-based Bezier curve estimation method to realize shape-aware model training. We improved the accuracy with the case of warm color, complex background, and finger touching that would be difficult to estimate target regions using color-based heuristics or traditional pixel-by-pixel methods. We prepared ground truth data for each finger region (index finger, middle finger, ring finger, little finger), then trained both the conventional pixel-by-pixel estimation method and our Bezier curve estimation methods. Quantitative results showed that the propos
ed models outperform traditional methods (pixel-wise IOU 0.935) and practical speed.
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