Iris segmentation is the process of defining the valid part of the eye image used for further processing (feature extraction, matching and decision making). Segmentation of the iris mostly starts with pupil boundary segmentation. Most pupil segmentation techniques are based on the assumption that the pupil is circular shape. In this paper, we propose a new pupil segmentation technique which combines shape, location and spatial information for accurate and efficient segmentation of the pupil. Initially, the pupil’s position and radius is estimated using a statistical approach and circular Hough transform. In order to segment the irregular boundary of the pupil, an active contour model is initialized close to the estimated boundary using information from the first step and segmentation is achieved using energy minimization based active contour. Pre-processing and post-processing were carried out to remove noise and occlusions respectively. Experimental results on CASIA V1.0 and 4.0 shows that the proposed method is highly effective at segmenting irregular boundaries of the pupil.
In this paper, a fast incremental image reduction principal component analysis approach (IIRPCA) is developed for
image representation and recognition. As opposed to traditional appearance based image techniques, IRPCA computes
the principal components of a sequence of image samples directly on the 2D image matrix incrementally without
estimating the covariance matrix. Therefore, IRPCA overcomes the limitations such as the computational cost and
memory requirements to making it suitable for real time applications. The feasibility of the proposed approach was tested
on a recently published large database consisting of over 2000 face images. IIRPCA shows superiority in terms of
computational time, storage and comparable recognition accuracy (94.0%) when compared to recent techniques such as
2DPCA (92.0%) and 2D RPCA (94.5%).
We develop a novel image feature extraction and recognition method two-dimensional reduction principal component analysis (2D-RPCA)). A two dimension image matrix contains redundancy information between columns and between rows. Conventional PCA removes redundancy by transforming the 2D image matrices into a vector where dimension reduction is done in one direction (column wise). Unlike 2DPCA, 2D-RPCA eliminates redundancies between image rows and compresses the data in rows, and finally eliminates redundancies between image columns and compress the data in columns. Therefore, 2D-RPCA has two image compression stages: firstly, it eliminates the redundancies between image rows and compresses the information optimally within a few rows. Finally, it eliminates the redundancies between image columns and compresses the information within a few columns. This sequence is selected in such a way that the recognition accuracy is optimized. As a result it has a better representation as the information is more compact in a smaller area. The classification time is reduced significantly (smaller feature matrix). Furthermore, the computational complexity of the proposed algorithm is reduced. The result is that 2D-RPCA classifies image faster, less memory storage and yields higher recognition accuracy. The ORL database is used as a benchmark. The new algorithm achieves a recognition rate of 95.0% using 9×5 feature matrix compared to the recognition rate of 93.0% with a 112×7 feature matrix for the 2DPCA method and 90.5% for PCA (Eigenfaces) using 175 principal components.