M. M. Kazi

Multimodal Biometric System Using Fingernail and Finger Knuckle

Over many decades lines on hands used for astrological and numerology analysis because there is a trust that Lines never lie. Dorsum of the hand can be very useful in personal identification but yet it has not that much extensive attention. Single scan of dorsum hand can give two biometric traits finger-knuckle and finger nail. This paper presents an approach to combine Finger-knuckle and finger-nail features. Finger nail biometric is considered as quite unique biometric trait hence we combine this trait with finger knuckle. Finger knuckle features are extracted using Mel Frequency Cepstral Coefficient (MFCC) technique and the features of finger-nail are extracted from second level wavelet decomposition. We combined these features using feature level fusion and feed forward back propagation neural network for classification. The performance of the system has been tested on our own KVKR-knuckle database that includes 100 subjects dorsal hands. Evaluation results shows that increase in training set gives increased performance rate. The best performance of the proposed system reaches up to 97% with respective training of 90% of total dataset.

Linguistic Divergence Patterns in English to Marathi Translation

In machine translation system, the text is translated from one language known as source language into another language known as target language. The development of a machine translation system needs to identify the patterns of divergence between two languages. A detail study of divergence issues in machine translation is required for their proper classification and detection. The primary objective of this paper is to understand the types of divergence problems that operate behind English to Marathi translation. In this paper, the various divergence patterns between English-Marathi language pair are considered. This will enable us to come up with strategies to handle these situations and coming up with correct translation. General Terms Natural Language Processing, Machine Translation, Computational Linguistics

Steganography Enhancement by Combining Text and Image through Wavelet Technique

rapidly proliferated information and evolution of digital technologies by Information hiding in multimedia data has improved the ease of access to digital information enabling reliable, faster and efficient storage, transfer and processing of digital data and leads to the consequence of making the illegal production and redistribution of digital media easy and undetectable. Hence, it poses a novel challenges for researchers. In this paper we present a novel integration of an incorporating text and image steganography to find a solution for enhancing security and protecting data. We proposed an enhancement of steganography algorithm which involves the scheme of discrete wavelet transformation combining text and image by secretly embed encrypted secrete message text data (cipher text) ortext image in the content of a digital image. The system based on levels of encryption and decryption methods performed to enhance the security of the system. Here first generate secrete message text (cipher text) or text image, and then processing deals with embedding and extracting Steganography algorithms. Finally the process deals with extraction of the hiddensecrete message. The experimental result shows a high level of efficiency and robustness of the proposed system.

Hyper spectral face image based biometric recognition

Day to day life is more unsecure as per the hacking is concerned. Our data is not secure because it can be stolen, hacked, destroy, manipulate, password may forget, guess, Card, token, etc. To overcome this problem biometrics is used as a strong authentication system. The person should be present at the time of the instance. Biometric security is challenging task in day to day life because it is difficult to avoid the fraud. In this research paper emerging biometric trait, i.e. Hyper Spectral Face is considered for human authentication system. There are various visible spectrum of electromagnetic spectral bands are considered for face recognition instead of only three RGB bands. Hyper Spectral gives band wise more finite detail information on face. It is very novel and more accurate than ordinary face recognition system. Hong Kong PolyU Hyper Spectral Face Database used for Face recognition. Kernel Principle Component Analysis (KPCA) algorithm gives prominent features of the Hyperspectral Face Dataset. Extracted features are classified by Mahalinobis Cosine (Mahcos) similarity measurement technique. The Recognition rate calculated on the basis of One Rank Level it furnishes 69.20%.

Multi sensor, multi algorithm based face recognition & performance evaluation

Biometric is emerging area in the computer science for the secure various systems. Day to day life peoples are preferred to use, robust and highly acceptable security system which can surpass the human errors. Many scientists are engaged to develop a strong biometric system, but there are a lot of challenges in the real time application. It is observed and found that researchers are only working on too old laboratory databases such as Olivetti Research Laboratory (ORL). But now a days various cost effective data acquisition sensors are coming on the market with high resolution of the data. When we are using a different type of data capturing devices gives the difference in performance of recognition rate. In this work we have proved that recognition rate is affected by the various sensors as well as database environment. For robust face recognition system suitable algorithms are suggested to different type of sensors.