Effective Descriptors based Face Recognition Technique for Robotic Surveillance Systems

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The aim is to propose SPHORB Face Recognition technique for Robotic Surveillance systems. Surveillance bots with efficient face recognition system becomes even more powerful and adds to the applications of the bots. In this paper we compare the results of face recognition using SPHORB (a new fast and robust binary feature detector and descriptor for spherical panoramic image) [1] algorithm with ORB (Oriented Fast and Rotated Brief) [4] algorithm for classifying 2D and 3D images Database. In the first part 2D face images are randomly selected from LFW [32] with good sample of images across gender and ethnicity. The number of keypoints that were identified and number of matching keypoints between the 2D images using SPHORB and ORB were compared. From the results we determine that the SPHORB algorithm has identified more keypoints for the same 2D image than ORB, it has processed more images but ORB gives better accuracy than SPHORB for 2D images. In the next part we investigated the image matching between a front face 2D image with a 3D image. The front face image and 3D images are randomly selected from the ThatsMyFace.com [2] with good sample of images across genders, ethnicity and age. The front face image is compared with a randomly selected 3D image from the sample class set using SPHORB and ORB algorithms. The number of keypoints that were identified and number of matching keypoints between images using SPHORB and ORB were compared. From the results we determine that the SPHORB algorithm has identified more keypoints for the same image than ORB, it has processed more images and has accuracy comparable to the ORB algorithm.