Article

Approach of human face recognition based on SIFT feature extraction and 3D rotation model

Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
06/2011; DOI: 10.1109/ICINFA.2011.5949039

ABSTRACT

One of the main problems in face recognition is the influences of varying poses and illumination. This paper proposes a novel method of human face recognition to overcome the influences. The method is mainly based on the SIFT feature extraction and 3D rotation model of heads. SIFT descriptor is used to select key points of faces in the database including seventy people with nine poses in the first stage. Then according to the feature of a test face, matching algorithm is applied to find its candidates from the database and defines some criteria to convince the final matching result in the second stage. If satisfactory results can not be gained in the second stage, the 3D rotation method will be triggered and it makes a secondary decision by normalizing the depth information of the faces. This algorithm is tested in the face database and the result shows that the accuracy is as high as 94.45%.

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    • "SIFT 알고리즘은 크기 변화에 강건한 후보점을 찾기 위하여 서로 다른 크기의 이미지에 가우시안 함수를 적용하여 후보점을 추출하고 추출한 후보점을 정제한 후 각 후보점에 대하여 방위와 크기를 할당한다. 이와 같은 국소 특징을 이용한 SIFT 알고리즘은 이미지의 회전, 크기변화에는 강건하게 적 용되지만, 인식하고자 하는 물체의 3차원적 회전 변화에는 동일한 물체일지라도 인식에 어려움이 있다[7][8]. 이는 국소 후보점을 정의하는 SIFT 기술자가 물체의 3차원 회전변화, 즉 투영변환(perspective transform)을 수반하는 포괄적인 변화에는 강인하지 않는 특성을 지니고 있기 때문이다. 위 문제를 해결하기 위해 SIFT특징점 개선을 위한 기존 의 SIFT 기술자에 추가적인 정보를 이용하여 인식하거나 SIFT 매칭 방법 개선, 이미지들의 기하학적 관계를 중심으 로 3D로 복원하는 연구가 진행되고 있다. "
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    ABSTRACT: 3D object recognition using only 2D images is a difficult work because each images are generated different to according to the view direction of cameras. Because SIFT algorithm defines the local features of the projected images, recognition result is particularly limited in case of input images with strong perspective transformation. In this paper, we propose the object recognition method that improves SIFT algorithm by using several sequential images captured from rotating 3D object around a rotation axis. We use the geometric relationship between adjacent images and merge several images into a generated feature space during recognizing object. To clarify effectiveness of the proposed algorithm, we keep constantly the camera position and illumination conditions. This method can recognize the appearance of 3D objects that previous approach can not recognize with usually SIFT algorithm.
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