Abstract-Biometrics is an advanced way of person recognition as it establishes more direct and explicit link with humans than passwords, since biometrics use measurable physiological and behavioural features of a person. In this paper gender recognition from human gait in image sequence have been successfully investigated. Silhouette of 15 males and 15 females from the database collected from CASIR site have been extracted. The computer vision based gender classification is then carried out on the basis of standard deviation, centre of mass and height from head to toe using Feed Forward Back Propagation Network with TRAINLM as training functions, LEARNGD as adaptation learning function and MSEREG as performance function. Experimental results demonstrate that the present gender recognition system achieve recognition performance of 93.4%, 94.6%, and 94.7% with 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons respectively. When the performance function is replaced with SSE the recognition performance is increased by 2%, 2.4% and 3% respectively for 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons.The above study indicates that Gait based gender recognition is one of the best reliable biometric technology that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations and even airports need to quickly detect threats and provide differing levels of access to different user groups.
Fingerprint identification system is mainly consisted of fingerprint achieving, fingerprint classification and fingerprint matching. Fingerprint matching is the key to the system and effects on the precision and efficiency of the whole system directly. Fingerprints are matched mainly based on their fingerprint texture pattern which can be described with the orientation field of fingerprints. A fingerprint, which has the different orientation angle structure in different local area of the fingerprint and has a texture pattern correlation among the neighboring local areas of the fingerprint, can be viewed as a Markov stochastic field. A novel method of fingerprint matching, which is based on embedded Hidden Markov Model (HMM) that is used for modeling the fingerprint's orientation field, is described in this paper. The accurate and robust fingerprint matching can be achieved by matching embedded Hidden Markov Model parameters which were built after the processing of extracting features from a fingerprint, forming the samples of observation vectors and training the embedded Hidden Markov Model parameters.
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