Department of Electrical and Electronics Engineering

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Author: search.filters.author.Ajmera, Pawan K.Subject: search.filters.subject.Image preprocessing

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    Palm-print recognition based on scale invariant features
    (IEEE, 2019) Ajmera, Pawan K.
    Over the past few years, palm-print recognition has proved to be one of the extensively used technology for human identification/verification in many aspects. This paper presents the implementation of five feature extraction algorithms such as Mean, AAD (Average Absolute Deviation), GMF (Gaussian Membership Function) along with SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Feature) for effective recognition. SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) are the machine learning algorithms used for the classification of data. Experimentations are carried out on the PolyU and IIT-Delhi palm-print databases. The scale invariant features of SURF provides the best performance with Correct Recognition Rate (CRR) of 99.56% and 97.95% for IIT-Delhi and PolyU palm-print database respectively.
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    Adaptive Quality Enhancement Fingerprint Analysis
    (IEEE, 2020) Ajmera, Pawan K.
    Poor quality of the fingerprint image prevents accurate recognition as the employed methods are largely dependent on the fingerprint image quality. Algorithms will be better suited to detect fingerprint images if they are adapted according to their quality classes. In this paper, a class adaptive fingerprint enhancement algorithm is presented, classes dry, good and wet are assigned and further image processing is carried out. Features such as mean, variance, moisture index, Ridge Valley Area Uniformity (RVAU) are extracted from the ROI images. There are two stages of fingerprint quality enhancements which include the quality preprocessing (QP) and the enhancement stage. Support Vector Machine (SVM) algorithm is used to classify the images. Further, comparison scores are calculated by comparing the given image with the database of the minutiae using the minutiae matching technique. Experimentation is carried out on the FVC fingerprint database. A comparative analysis of the fuzzy C-means based clustering and mean based clustering is also experimented.
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    Face Recognition using Local Texture Descriptor
    (IJERT, 2014-04) Ajmera, Pawan K.
    A face recognition system is a computer application for automatically identifying a person from a digital image. Recognition of face in uncontrolled lightening situations is one of the most important bottlenecks for practical face recognition systems. This paper addresses the problem of illumination effects on Face recognition and work for an approach to reduce their effect on recognition performance. For this following methods are used: (i) simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) Local Binary Pattern (LBP) texture descriptor which labels the pixels of an image and gives output as a histogram of image; and (iii) principle component analysis (PCA) feature extraction algorithm is used to improve robustness. The proposed method is tested on ORL face database. The crux of the work lies in optimizing Euclidean distance classifier for recognition of face.