The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

The ear, as a biometric, has been given less attention, compared to other biometrics such as fingerprint, face and iris. Since it is a relatively new biometric, no commercial applications involving ear recognition are available. Intensive research in this field is thus required to determine the feasibility of this biometric. In medical field, especially in case of accidents and death, where face of patients cannot be recognized, the use of ear can be helpful. In this work, yet another method of recognizing people through their ears is presented. Local Binary Patterns (LBP) is used as features and the results are compared with that of Principal Components Analysis (PCA). LBP has a high discriminative power, tolerance against global illumination changes and low computational load. Experiments were done on the Indian Institute of Technology (IIT) Delhi ear image database and results show that LBP yields a recognition rate of 93 % while PCA gives only 85 %.

Face recognition is an active area of biometrics. This study investigates the use of Chain Codes as features for recognition purpose. Firstly a segmentation method, based on skin color model was applied, followed by contour detection, then the chain codes of the contours were determined. The first difference of chain codes were calculated since the latter is invariant to rotation. The features were calculated and stored in a matrix. Experiments were performed using the University of Essex Face database, and results show a recognition rate of 95% with this method, when compared with Principal Components Analysis (PCA) giving 87.5% recognition rate.

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Biometrics is playing a major role in automating personal identification system deployed to enhance security in several applications including use of passports, cellular telephones, automatic teller machines, computer systems and driver licenses. The use of biometric features for identification purposes requires that a particular biometric factor be unique for each individual, that it can be readily measured, and that it is invariant over time. In this work, a review of biometrics is made, including the characteristics of biometrics. The existing biometric technologies have been detailed with their relative strengths and weaknesses. Finally, applications using biometrics have been outlined with the view to understand that not all biometric are suitable for all the applications, each application has its requirements and the right biometric chosen will enhance the security of that application.