التعرف على الأشخاص باستخدام بصمة اليد يلقى الكثير من الاهتمام بالتزامن مع الحاجة إلى تقنيات جديدة ترفع من مستوى الأمان. في هذه الدراسة تم اقتراح تقنية جديدة للتعرف على الأشخاص عن طريق بصمة اليد و ذلك من خلال استخلاص السمات من معاملات التحويل المويجي لصور راحة اليد بالاعتماد على فكرة التقاطعات الصفرية (عدد مرات التقاطع مع القيمة صفر). حيث تم إيجاد التحويل المويجي عند المستوى الرابع لكامل صورة اليد و الذي نتج عنه أربع مصفوفات، ثلاث مصفوفات تفاصيل (أفقية – شاقولية- قطرية) و مصفوفة تقريبات و تم الاعتماد على مصفوفات التفاصيل دون التقريبات لأن المعلومات التي نحتاجها (خطوط و منحنيات اليد) محتواة في مصفوفات التفاصيل. بعد ذلك تم استخلاص ستة عشر معامل (سمة ) من كل مصفوفة تفاصيل و ترتيب هذه السمات ضمن شعاع واحد ليتشكل شعاع السمات المستخلص من كل عينة من عينات اليد و المكون من ثمان و أربعين (48) سمة و الذي تم استخدامه كدخل للشبكة العصبونية المستخدمة. تم خلال هذه الدراسة بناء قاعدة بيانات مكونة من 400 صورة لراحة اليد عائدة لأربعين شخص بمعدل 10 صور لكل شخص. حيث أظهرت الاختبارات العملية أن النظام المصمم نجح في التعرف بمعدل 91.36%.
Personal identification based on handprint has been gaining more attention with the
increasing needs of high level of security. In this study a novel approach for human
recognition based on handprint is proposed. Wavelet transform was used to extract features
presented in the palm image based on wavelet zero-crossing method. Firstly the wavelet
transform of the whole palm image at the fourth level was worked out, which results in
four matrices; three of them are detail matrices (i.e., horizontal, vertical and diagonal) as
well as one approximation matrix. Throughout this study, only the detail matrices were
used because the required information (i.e., hand lines and curves) is included in those
matrices. Sixteen features were extracted from each detail matrix, and then arranged in one
vector. Consequently, for each palm sample a feature vector consisting of 48 input features
of the used neural network was obtained. For this purpose, a database consisting of 400
palm images belonging to 40 people at the rate of 10 images per person was built. Practical
tests outcome showed that the designed system successfully indentified 91.36% of the
tested images.
References used
KAUR, G., SINGH, G. AND KUMAR, V. A Review on Biometric Recognition. International Journal of Bio-Science and Bio-Technology, 6(2014), 69-76
ZHANG, D., KONG, W.K., YOU,J. and WONG, M. Biometrics online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003), 1041-1050
WOODARD, J. D., ORLANS, N. M., and HIGGINS, P. T." Biometric:Identity Assurance in the Information Age", McGraw-Hill, New York, 2003. pp: 45-115
This paper introduces a new approach to extract palm print features
and select the best ones. The paper also studies the effectiveness of
the selection process on speed and performance of system.
This study was made at ALASSAD Hospital-TISHREEN University in-LATTAKIA
at the Department of Obestetrics and Gynecology in the period between 1/1/2013 and
1/1/2014. The number of patients the study was 190.Including140 cases have been holding
memb
Traffic Conflict Technique TCT has a long history in traffic safety researches. Traffic accidents are now
well known to generate a serious problem that exhausts enormous resources of the national economy
either directly or indirectly. Because signa
The speech recognition is one of the most modern technologies, which entered force
in various fields of life, whether medical or security or industrial techniques. Accordingly,
many related systems were developed, which differ from each otherin fea
In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels.