Company Filing History:
Years Active: 2020
Title: Innovations by Jun-Shian Hsiao
Introduction
Jun-Shian Hsiao is a notable inventor based in Chupei, Taiwan. He has made significant contributions to the field of optical identification technology. With a total of 2 patents, Hsiao's work focuses on methods that enhance the accuracy and reliability of physiological feature identification.
Latest Patents
Hsiao's latest patents include an "Optical Identification Method and Optical Identification System." This innovative optical identification method involves projecting light onto a physiological portion to generate reflection light. The process includes receiving the reflection light to create an image, generating slant pattern information from the image, transforming this information into a pattern identification matrix, and ultimately determining the physiological feature based on the matrix. Another patent, simply titled "Optical Identification Method," describes a technique where light is projected onto a finger to generate reflected light. This method utilizes a pixel sensing array to obtain multiple finger images and assesses whether these images exhibit a liveness characteristic based on exposure time or average brightness. If the images show the liveness characteristic, identification information is determined; otherwise, the process may halt further image acquisition.
Career Highlights
Throughout his career, Jun-Shian Hsiao has worked with companies such as Beyond Time Investments Limited. His experience in these organizations has contributed to his expertise in optical technologies and innovations.
Collaborations
Hsiao has collaborated with notable individuals in his field, including Chu-Hsin Chang and Chun-Fu Lin. These partnerships have likely enriched his work and led to further advancements in optical identification methods.
Conclusion
Jun-Shian Hsiao's contributions to optical identification technology demonstrate his innovative spirit and commitment to enhancing identification methods. His patents reflect a deep understanding of physiological feature sensing, paving the way for future advancements in this area.