A core characteristics based human face recognition method under the condition of illumination is proposed according to the problem of the sharply declining human face recognition rate under the condition of lighL Wit...A core characteristics based human face recognition method under the condition of illumination is proposed according to the problem of the sharply declining human face recognition rate under the condition of lighL With this method, if human face image is affected by light and the illumination is forward or side can be judged; the images affect by illumination are processed using the strategy of frequency domain replacement, and then the key areas of human face image are divided and then are recognized using support vector machine （SVM） based on the unit of area, and finally the recognition results are integrated. The experimental result shows that this method can produce a better recognition effect than other methods in view of the problem of illumination.展开更多
The scale invariant feature transform （SIFT） feature descriptor is invariant to image scale and location, and is robust to affine transformations and changes in illumination, so it is a powerful descriptor used in m...The scale invariant feature transform （SIFT） feature descriptor is invariant to image scale and location, and is robust to affine transformations and changes in illumination, so it is a powerful descriptor used in many applications, such as object recognition, video tracking, and gesture recognition. However, in noisy and non-rigid object recognition applications, especially for infrared human face recognition, SIFT-based algorithms may mismatch many feature points. This paper presents a star-styled window filter-SIFT （SWF-SIFT） scheme to improve the infrared human face recognition performance by filtering out incorrect matches. Performance comparisons between the SIFT and SWF-SIFT algorithms show the advantages of the SWF-SIFT algorithm through tests using a typical infrared human face database.展开更多
文摘A core characteristics based human face recognition method under the condition of illumination is proposed according to the problem of the sharply declining human face recognition rate under the condition of lighL With this method, if human face image is affected by light and the illumination is forward or side can be judged; the images affect by illumination are processed using the strategy of frequency domain replacement, and then the key areas of human face image are divided and then are recognized using support vector machine （SVM） based on the unit of area, and finally the recognition results are integrated. The experimental result shows that this method can produce a better recognition effect than other methods in view of the problem of illumination.
文摘The scale invariant feature transform （SIFT） feature descriptor is invariant to image scale and location, and is robust to affine transformations and changes in illumination, so it is a powerful descriptor used in many applications, such as object recognition, video tracking, and gesture recognition. However, in noisy and non-rigid object recognition applications, especially for infrared human face recognition, SIFT-based algorithms may mismatch many feature points. This paper presents a star-styled window filter-SIFT （SWF-SIFT） scheme to improve the infrared human face recognition performance by filtering out incorrect matches. Performance comparisons between the SIFT and SWF-SIFT algorithms show the advantages of the SWF-SIFT algorithm through tests using a typical infrared human face database.
