Face Verification with Three-Dimensional Point Cloud by Using Deep Belief Networks
碩士 === 國立臺北大學 === 資訊工程學系 === 103 === Developing face recognition systems has been a challenge for decades. The variation in illumination and head pose may decrease the accuracy of two-dimensional face recognition. With the invention of a depth map sensor, more three-dimensional volume data can be pr...
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| Format: | Others |
| Language: | en_US |
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2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/45788712097104409134 |
| Summary: | 碩士 === 國立臺北大學 === 資訊工程學系 === 103 === Developing face recognition systems has been a challenge for decades. The variation in illumination and head pose may decrease the accuracy of two-dimensional face recognition. With the invention of a depth map sensor, more three-dimensional volume data can be processed to mitigate the problem associated with face verification. This paper describes our three-dimensional face verification approach in three phases. First, point cloud library is applied to estimate normal vectors and principal curvatures of every point on a human face point cloud acquired from three-dimensional depth sensor. Next, we adopt deep belief networks to train the identification model using estimated features. Then, face verification is accomplished by using the pre-trained deep belief networks to justify if new incoming face point cloud feature is the one we specified. The experimental results demonstrate that the proposed system performs up to 95% verification accuracy.
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