Aiming at the characteristics of edge gradient features and orientation features of palmprint images, a new Local Joint Edge and Orientation Patterns (LJEOP) method is proposed to extract palmprint features. Firstly, the Kirsch operator utilizes calculate the edge response values of palmprint images in 8 different orientations and the Local Maximum Edge Pattern(LMEP) is proposed to represent the edge features. The orientation features of the palmprint image are extracted by using a Gabor filter or a Modified Finite Radon Transform (MFRAT). Then the joint analysis of edge features and orientation features is carried out to construct a two-dimensional feature matrix. Compared with some existing palmprint recognition methods, our experimental results on the MSpalmprint library achieve higher recognition rate ,lower equal error rate and faster recognition speed.
The Gabor transform has been recognized as being very useful in diverse areas such as speech and image processing, radar, sonar and seismic data processing and interpretation; however, its real time applications were limited due to its high computational complexity. To reduce the computational complexity, the real-valued discrete Gabor transform (RDGT) was presented in our previous work. In this paper, firstly, the 2-D RDGT and its simple relationship with the 2-D complex-valued discrete Gabor transform (CDGT) will be briefly reviewed; secondly, time-recursive algorithms for the efficient and fast computation of the 2-D RDGT coefficients of an image and for the fast reconstruction ofthe original image from the coefficients will be developed; thirdly, two-layer parallel lattice strLictures for the implementation of the algorithms will be studied; and finally, the computational complexity of the proposed algorithms will be analyzed and compared with that of the existing 2-D CDGT algorithms, which points out that the parallel implementation ofthe proposed algorithms are very attractive for real time image processing.
This paper presents a novel algorithm for automatic localization of human eyes in grayscale still images with complex background based on geometrical facial features and image segmentation,. First of all, a determination criterion of eye location is established by the priori knowledge of geometrical facial features. Secondly, a range of threshold values that would separate eye blocks from others in a segmented facial image is estimated from the facial image histogram. Thirdly, with the progressive increase of the threshold by an appropriate step in that range, the size of the existing blocks in the segmented facial image will expand, some existing blocks will merge into one block, and some new blocks will emerge. Once two eye blocks appear from the segmented image, they will be detected by the determination criterion of eye location. Finally, the 2-D correlation coefficient is used as a symmetry similarity measure to check the factuality of the two detected eyes. In this way, the optimal threshold value can be automatically found based on the result of detection such that eyes can be accurately located. The experimental results demonstrate the high efficiency of the algorithm in runtime and correct localization rate.
By replacing the complex-valued Gabor basis functions of the complex-valued discrete Gabor transforms (CDGTs) with real-valued Gabor basis functions, we propose fast algorithms for 1 -D and 2-D real-valued discrete Gabor transforms (RDGTs) in this paper. The RDGT algorithms provide a simpler method than the CDGT algorithms to calculate the transform (or Gabor) coefficients of a signal or an image from finite summations and to reconstruct the original signal or image exactly from the computed transform coefficients. The similarity between the RDGTs and the discrete Hartley transforms (DHTs) enables the RDGTs to utilize the fast DHT algorithms for fast computation. Moreover, the RDGTs have a simple relationship with the CDGTs such that the CDGT coefficients can be directly computed from the RDGT coefficients.
An advanced Independent Component Analysis (ICA) algorithm based on genetic algorithm is proposed with analysis to the ICA method. The proposed algorithm can be used to solve the problem of local optimum that is easily stacked into by normal numerical solution. The image separation simulation shows that the global optimum can be acquired through the proposed algorithm under the circumstance of adequate colony size and genetic generations.