Siddharth B. Dabhade

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.

Fingerprint and Palmprint Recognition using Neighborhood Operation and FAST Features

In this paper presents biometrics based fingerprint and palmprint recognition and authentication system. Fingerprint and palmprint images are enhanced using preprocessing techniques such as morphological operations. The feature extraction techniques such as neighborhood operation and FAST feature algorithm is used to independently extract fingerprint and palmprint features. These techniques are more reliable and faster than traditional techniques used. Experimental results shown recognition rate 89.29% for fingerprint and 100% for palmprint this implies that the proposed methodology has better performance and is more reliable over the techniques proposed and used earlier. Abbrevations SF Single Flat SFF Single Flat Flexi LT Left Thumb finger LI Left Index finger LM Left Middle finger LR Left Ring finger LL Left Little finger

Feature Selection for Heart Rate Variability Based Biometric Recognition Using Genetic Algorithm

Heart Rate Variability (HRV) is a prominent property of heart, so far utilized by medical community for diagnostic and prognostic purpose. There was an early attempt to employ HRV for biometric recognition purpose however due to lack of information, the methodologies applied, features used, and results obtained are not available for reference and comparison. In this article we attempt to utilize HRV for biometric purpose, and subsequently obtained 101 most commonly used HRV features. These features have been identified in the guidelines framed by the especially constituted taskforce of European Society of Cardiology and North American Society of Pacing and Electrophysiology for standardization of HRV related studies. Biometric recognition system depends basically on some strongly discriminative elements in a feature vector for accurately distinguishing individuals. The large feature vector of 101 features in addition to the useful ones, may definitely have irrelevant and redundant features. Therefore features selection becomes a crucial step before classification is attempted and feature selection from a large feature sets, cannot be done arbitrarily. The main intention of this article is to identify prominent features of HRV data that can be employed in biometric recognition. For this purpose we applied Genetic Algorithm (GA) which utilizes adaptive search techniques and have documented significant improvement on variety of search problems. GA proposed 15 prominent features out of 101. Performance analysis with the identified features is presented along with the recognition rate.

Human Electroencephalographic Biometric Person Recognition System

Human head generates various signals according to the situation and activates inside the head as well as outside the head. The frequency of the Head Signal means that brain signal is different as per the level of action taken place by the person; it may be either be imaginary or motor imagery activities. From the brain signals imaginary signals are captured using MindWave Mobile Portable device. Frequency-wise channels are separated and categories as Delta, Theta, Alpha, and Beta. These channels indicated emotions, movement, sensations, vision, etc. Features are extracted of each channel using Power Spectral Density (PSD) function and Deep learning Neural Network. Feature level fusion is used for pattern matching. The Novelty of this work is a single electrode device that is used to capture an Electroencephalography (EEG) imaginary data from the head which is generated by brain functioning. The feature level fusion of channels and Deep learning Neural Network classification of feature give better performance. The results are proven that these EEG imaginary signals could be used as better biometrics-based authentication 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.