Palm Print Recognition Using Wavelet Transforms

Abstract

Palmprint recognition is a biometric technology that has gained popularity in recent years due to its great accuracy and reliabilityin identifying people based on the distinctive patterns seen in their hands' palms. Wavelet transform is a signal and image processing mathematical tool that has been applied to palmprint recognition to extract features from palmprint images, which are subsequently used for recognition. The wavelet transform technique for palmprint recognition begins with the collection of a high-resolution image of the palmprint, which is then preprocessed to eliminate any noise or artefacts. The image is then subjected to the wavelet transform, which divides it into a series of sub-bands with varying sizes and resolutions. Features unique to the individual's Palmprint are derived from each sub-band. To identify the individual, the retrieved traits are compared to the features contained in the database. Since palmprints are unique to each individual and do not vary considerably over time, Palmprint recognition using wavelet transform is a non-intrusive technology with excellent accuracy and reliability. Overall, using wavelet transform is a promising biometric approach with applications in security and access control, forensic investigation, and healthcare.