multimodal biometric recognition using particle swarm optimization-based selected features
سال انتشار: 1392
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 907
فایل این مقاله در 10 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
این مقاله در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JIST-1-2_002
تاریخ نمایه سازی: 21 فروردین 1393
چکیده مقاله:
Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.
کلیدواژه ها:
Biometric ، Genetic Algorithm (GA) ، Particle Swarm Optimization (PSO) ، Discrete Cosine Transform (DCT) ، Discrete Wavelet Transform (DWT)
نویسندگان
sara Motamed
Artificial Intelligence, Ph.D. Student, Computer and Science, Islamic Azad University, Fuman Branch
Ali Broumandnia
Artificial Intelligence, Assistant Professor, Computer Department, Islamic Azad University, South Tehran Branch
Azamossadat Nourbakhsh
Artificial Intelligence, Ph.D. Student, Computer Department, Islamic Azad University, Lahijan Branch