Takuya Adachi

A single-chip fingerprint sensor and identifier

A chip architecture that integrates a fingerprint sensor and an identifier in a single chip is proposed. The fingerprint identifier is formed by an array of pixels, and each pixel contains a sensing element and a processing element. The sensing element senses capacitances formed by a finger surface to capture a fingerprint image. An identification is performed by the pixel-parallel processing of the pixels. The sensing element is built above the processing element in each pixel. The chip architecture realizes a wide-area sensor without a large increase of chip size and ensures high sensor sensitivity while maintaining a high image density. The sensing element is covered with a hard film to prevent physical and chemical degradation and surrounded by a ground wall to shield it. The wall is also exposed on the chip surface to protect against damage by electrostatic discharges from the finger contacting the chip. A 15/spl times/15 mm/sup 2/ single-chip fingerprint sensor/identifier LSI was fabricated using 0.5-/spl mu/m standard CMOS with the sensor process. The sensor area is 10.1/spl times/13.5 mm/sup 2/. The sensing and identification time is 102 ms with power consumption of 8.8 mW at 3.3 V. Five hundred tests confirmed a stranger-rejection rate of the chip of more than 99% and a user-rejection rate of less than 1%.

A fingerprint verification algorithm using the differential matching rate

We propose a fingerprint verification algorithm with template matching based on the cyclic structure of the fingerprint image. For a correlation value between a template and a sensed image, we use the differential matching rate. The differential matching rate utilizes the cyclic structure observed in the local area of a fingerprint pattern, which is calculated by the maximum matching rate minus the minimum matching rate detected near the point where the matching rate is maximum. In order to extract the maximum effectiveness of the cyclic structure, we compare divided windows of a template image against a sensed image. A verification experiment confirmed that the proposed method judges whether a template image and a sensed image are same fingers fingerprints more accurately than the conventional method using the maximum matching rate as a correlation value. We obtain the equal error rate (EER) of 3.6%, which is better than half that of the conventional method.

Fingerprint image enhancement by pixel-parallel processing

This paper proposes a fingerprint image enhancement algorithm that can be mapped onto a compact pixel-parallel architecture, such as a fingerprint identification chip that senses and identifies fingerprints by itself. The algorithm is composed of two parts. First, ridges are enhanced by extracting their center lines and removing white noises using the center line image. Then valleys are enhanced by detecting areas where they are thin and disconnected and dilating them at these areas. Both enhancements are done with the structures of the fingerprint maintained. The experimental results show fingerprint identification using the proposed image enhancement algorithm improves the error ratio of that the conventional method, and the execution time is 0.14 msec when the proposed algorithm is performed on the array of processing elements on the fingerprint identification chip.