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초록

영어

Three-dimensional (3D) face models can handle large pose face recognition problem. In this paper, we propose survey on pose-invariant face recognition method. By scrutinizing study depth, here we only analysis on pose invariant technique in face recognition method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images of the face in the database can be the same view as the probe during surveillance. In this survey of face detection technology has been presented in this work feature based approach with low level analysis has been done. Information, color, shape, model etc. has been considered for investigation. The technique considered here also explained in other papers. Depth information has improved the performance of face recognition with large pose variations up to the mark able angle and under even more challenging conditions.

목차

Abstract
 1. Introduction
 2. Literature Survey
 3. Sensitive Analysis
 4. Algorithms Analysis
 5. Future Research Direction
 6. Conclusion
 References

키워드

face recognition face detection pose invariant virtual frontal view template matching PCA Three-dimensional (3D) Two-dimensional (2D) SVM ANN CNN Edge detection.

저자

• Deepika Dubey [ Business Administration Research Institute, Sungshin W. University, Bomun-ro 34da-gil, Seongbuk-gu, Seoul, Korea ]
• G.S Tomar [ Business Administration Research Institute, Sungshin W. University, Bomun-ro 34da-gil, Seongbuk-gu, Seoul, Korea ]
• Hye-jin Kim [ Business Administration Research Institute, Sungshin W. University, Bomun-ro 34da-gil, Seongbuk-gu, Seoul, Korea ]

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