https://maxwell.ict.griffith.edu.au/cvipl/pdfs/PR2003.pdf

Noisy logo recognition using line segment Hausdorff distance

A system chip targeting image and voice processing and recognition application domains is implemented as a representative of the potential of using programmable logic in system design. It features an embedded reconfigurable processor built by joining a configurable and extensible processor core and an SRAM-based embedded field-programmable gate array (FPGA). Application-specific bus-mapped coprocessors and flexible input/output peripherals and interfaces can also be added and dynamically modified by reconfiguring the embedded FPGA. The architecture of the system is discussed as well as the design flows for pre- and post-silicon design and customization. The silicon area required by the system is 20 mm/sup 2/ in a 0.18-/spl mu/m CMOS technology. The embedded FPGA accounts for about 40% of the system area.

A reconfigurable system featuring dynamically extensible embedded microprocessor, FPGA, and customizable I/O

We describe a novel approach for creating a three-dimensional (3-D) face structure from multiple image views of a human face taken at a priori unknown poses by appropriately morphing a generic 3-D face. A cubic explicit polynomial in 3-D is used to morph a generic face into the specific face structure. The 3-D face structure allows for accurate pose estimation as well as the synthesis of virtual images to be matched with a test image for face identification. The estimation of a 3-D person's face and pose estimation is achieved through the use of a distance map metric. This distance map residual error (geometric-based face classifier) and the image intensity residual error are fused in identifying a person in the database from one or more arbitrary image view(s). Experimental results are shown on simulated data in the presence of noise, as well as for images of real faces, and promising results are obtained.

3-D face structure extraction and recognition from images using 3-D morphing and distance mapping

This paper presents a novel biometric watermarking algorithm for improving the recognition accuracy and protecting the face and fingerprint images from tampering. Multi-resolution Discrete Wavelet Transform is used for embedding the face image in a fingerprint image. An intelligent learning algorithm based on υ-Support Vector Machine (SVM) is introduced to enhance the quality of the extracted face image. The performance of the watermarking algorithm is experimentally validated using existing fingerprint and face recognition algorithms. The results show that the extracted fingerprint and face images are of high quality. The use of SVM enhances the performance of face recognition by at least 10% even when the watermarked image is subjected to certain geometric and frequency attacks such as scaling, cropping, compression and filtering.

Improving biometric recognition accuracy and robustness using DWT and SVM watermarking

Biometrics is a growing technology, which has been widely used in forensics, secured access and prison security. A biometric system is fundamentally a pattern recognition system that recognizes a person by determining the authentication by using his different biological features i.e. Fingerprint, retina-scan, iris scan, hand geometry, and face recognition are leading physiological biometrics and behavioral characteristic are Voice recognition, keystroke-scan, and signature-scan. In this paper different biometrics techniques such as Iris scan, retina scan and face recognition techniques are discussed. Keyword: Biometric, Biometric techniques, Eigenface, Face recognition.

Biometrics and Face Recognition Techniques

Much research in human face recognition involves fronto-parallel face images, constrained rotations in and out of the plane, and operates under strict imaging conditions such as controlled illumination and limited facial expressions. Face recognition using multiple views in the viewing sphere is a more difficult task since face rotations out of the imaging plane can introduce occlusion of facial structures. In this paper, we propose a novel image-based face recognition algorithm that uses a set of random rectilinear line segments of 2D face image views as the underlying image representation, together with a nearest-neighbor classifier as the line matching scheme. The combination of 1D line segments exploits the inherent coherence in one or more 2D face image views in the viewing sphere. The algorithm achieves high generalization recognition rates for rotations both in and out of the plane, is robust to scaling, and is computationally efficient. Results show that the classification accuracy of the algorithm is superior compared with benchmark algorithms and is able to recognize test views in quasi-real-time.

/pdf/line-based-face-recognition-under-varying-pose-al1um9up5v.pdf

O. De Vel

Papers

Line-based face recognition under varying pose