Identification of people among each other has always been a tough and challenging task for the researchers. There are many techniques which are used for identifying a person but biometric technique is the standard one which allows us for online identification of individuals on the basis of their physiological and behavioral features. The veins based systems include finger veins, face veins, palm veins, head veins, heart veins, iris, palatal veins of the rogue etc. The multi-veins based systems use the veins of different physiological traits for identifying a person.
This paper illustrates an overview of veins based personal identification systems. The performance of different single and multi-veins based identification systems are analyzed in this paper. The features like reliability, security, accuracy, robustness and long term stability along with the strengths and weaknesses of various veins based biometric approaches were taken into considerations while analyzing the results of existing research papers published so far. At last the future research directions in the field of veins based identification systems have also been outlined.

With the advent of new thumbprint identification techniques, accurate personal identification is now easy and cheaper with approximately zero false acceptance rates. This paper focuses on developing an advance feature for thumbprint based identification systems with the help of soft computing and 2D transformation which makes the technique more flexible and Fidel. The thumbprint images of individuals were scanned with the help of H3 T&A terminal for collecting self generated datasets. The thumbprints of self generated and standard datasets were trained to form a refined set which includes linear and angular displacements of thumbprint images. The new obtained features of refined datasets were stored in the database for further identification.
In the proposed technique, the minutiae coordinates and orientation angles of the thumbprint of a person to be identified are computed and merged together for comparison. The minutia coordinates and orientation angles of a person are compared with the minutiae trained set values stored in the database at different linear and angular rotations for identity verification. The proposed technique was tested on fifty persons self generated and standard datasets of FVC2002, FVC2004 and CASIA databases. In the experimentation and result analysis we observed that the proposed technique accurately identifies a person on the basis of minutiae features of a thumbprint with low FNMR (False Non-Match Rate) values.

Biometric system is an alternative way to the traditional identity verification methods. This research article provides an overview of recently / currently used single and multiple biometrics based personal identification systems which are based on human physiological (such as fingerprint, hand geometry, head recognition, iris, retina, face recognition, DNA recognition, palm prints, heartbeat, finger veins, footprints and palates) and behavioral (such as body language, facial expression, signature verification and speech recognition) characteristics. 
This paper focuses on RGB based palatal pattern analysis of persons and the proposed technique uses RGB values with silhouette computes of palatal patterns for identifying a person. We have tested our proposed technique for palatal patterns of 50 persons including males & females and it is observed that RGB values based silhouette technique are accurately identifying the persons on the basis of their palatal patterns. For each person seven palatal images were taken. Out of these seven palatal images, four images were used for training dataset and last three palatal patterns were used for identifying the persons. The proposed technique is reliable & secure and it is a foolproof method which is clearly differentiating the persons on the basis of their palatal patterns.

Face veins based personal identification is a challenging task in the field of identity verification of a person. It is because many other techniques are not identifying the uniqueness of a person in the universe. This research paper finds the uniqueness of a person on the basis of face veins based technique. In this paper five different persons face veins images have been used with different rotation angles (left/right 900 to 2700 and 3150). For each person, eight different images at different rotations were used and for each of these images the same minimum cost minutiae tree (MCMT) is obtained. Here, Prim’s or Kruskal’s algorithm is used for finding the MCMT from a minutiae graph.
The MCMT is traversed in pre-order to generate the unique string of vertices and edge lengths. We deviated the edge lengths of each MCMT by five pixels in positive and negative directions for robustness testing.
It is observed in our experiments that the traversed string which consists of vertices and edge lengths of MCMT is unique for each person and this unique sequence is correctly identifying a person with an accuracy of above 95%. Further, we have compared the performance of our proposed technique with other standard techniques and it is observed that the proposed technique is giving the promising result.

In the recent year gesture recognition has become the most intuitive and effective communication technique for human interaction with machines. In this paper we are going to work on hand gesture recognition and interpret the meaning of it from video sequences. Our work takes place in following three phases: 1. Hand Detection & Tracking 2. Feature extraction 3. Gesture recognition. We have started proposed work with first step as applying hand tracking and hand detection algorithm to track hand motion and to extract position of the hand. Trajectory based features are being drawn out from hand and used for recognition process and hidden markov model is being design for each gesture for gesture recognition. Hidden Markov Model is basically a powerful statistical tool to model generative sequences. Our method is being tested on our own data set of 16 gestures and the average recognition rate we have got is 91%. With proposed methodology gives the better recognition results compare with the traditional approaches such as PCA, ANN, SVM, DTW and many more.

Identical twins identification is a challenging task because they share the same DNA sequence. This research paper presents minutiae coordinates and orientation angles fusion based technique for thumbprint identification of identical twins. Six different thumbprint images of identical twins were taken at a fixed time interval using H3 T&A terminal. The minutiae coordinates and orientation angles of these thumbprints were fused to form a union set. The union set values were stored in the smartcard memory for further identification.
The minutiae coordinates and orientation angles of a thumbprint of the person to be identified are computed and fused together for online identification. The fused minutiae are compared with the minutiae union set values stored in the smartcard memory for identity verification. The proposed method was tested on a self generated identical twin dataset and 50 identical twins of standard FVC04 and FVC06 datasets. We observed in experiments that the proposed method is accurately differentiating the identical twins of self generated and FVC datasets.

The twins are classified into two categories namely fraternal and identical twins. Fraternal twins differ in face structures and DNA sequences but, identical twins have the same face structure and share same DNA sequence. Therefore, it is difficult to identify identical twins on the basis of their faces and DNA sequences. In this research paper, we have introduced a new approach for identifying identical twins on the basis of minutiae coordinates, orientation angles, and minutiae distances of their thumbprint images. 
We tested the proposed method on thumbprint images of an identical twin pair generated by using Incept H3 T&A Terminal and fifty pairs of identical twins of FVC04, and FVC06 datasets. We have found that the proposed approach is superior, and robust in comparison to existing techniques in terms of accuracy, efficiency, and storage space.

Biometric systems are alternates to the traditional identification systems. This paper provides an overview of single feature and multiple features based biometric systems, including the performance of physiological characteristics (such as fingerprint, hand geometry, head recognition, iris, retina, face recognition, DNA recognition, palm prints, heartbeat, finger veins, palates etc) and behavioral characteristics (such as body language, facial expression, signature verification, speech recognition, Gait Signature etc.).
The fingerprints, iris image, and DNA features based multimodal systems and their performances are analyzed in terms of security, reliability, accuracy, and long-term stability. The strengths and weaknesses of various multiple features based biometric approaches published so far are analyzed. The directions of future research work for robust personal identification is outlined.