Milind E. Rane

Bio: Milind E. Rane is an academic researcher from Vishwakarma Institute of Technology. The author has contributed to research in topics: Biometrics & Facial recognition system. The author has an hindex of 11, co-authored 29 publications receiving 253 citations.

Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition

Abstract: CNNs have rapidly become state-of-the-art frameworks for various applications used in image classification. We typically need big, ground-based training data set so as to use revolutionary techniques in making a model more complex without affecting its accuracy and precision. In this case study, we shall discuss the different approaches and measures that are used in pattern recognition using CNN. We shall also discuss some of the major applications used nowadays in pattern recognition as well as their implementation (outputs only). We shall identify the key features used in building that application as well as its roles in making the application fit from any obstacles such as over-fitting, etc.

Multimodal score level fusion for recognition using face and palmprint

Abstract: The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biom...

Offline handwritten devanagari script segmentation

Abstract: The process of segmentation is a vital part in any script/character recognition technique. Devanagari is mostly useful Script in India for number of officials and banking applications. Segmentation of Devanagari script is difficult because of presence of large character set which include vowels, consonants, compound characters and modifiers. This paper focus on the line, word, character segmentation of handwritten Devanagari script for efficient script recognition.

International journal of electronics and communication engineering & technology (ijecet)

Abstract: A wide variety of systems require reliable person recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user and no one else access the rendered services. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. Face can be used as Biometrics for person verification. Face is a complex multidimensional structure and needs a good computing techniques for recognition. We treats face recognition as a two-dimensional recognition problem. A well-known technique of Principal Component Analysis (PCA) is used for face recognition. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by Eigen face which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, lips. The system performs by projecting pre extracted face image onto a set of face space that represent significant variations among known face images. The variable reducing theory of PCA accounts for the smaller face space than the training set of face. A Multire solution features based pattern recognition system used for face recognition based on the combination of Radon and wavelet transforms. As the Radon transform is in-variant to rotation and a Wavelet Transform provides the multiple resolution. This technique is robust for face recognition. The technique computes Radon projections in different orientations and captures the directional features of face images. Further, the wavelet transform applied on Radon space provides multire solution features of the facial images. Being the line integral, Radon transform improves the low-frequency components that are useful in face recognition.

Survey of Palmprint Recognition

Abstract: A biometric system is essentially a pattern recognition system which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user Biometric has gained much attention in the security world recently Many types of personal identification systems have been developed and palmprint verification is one of the emerging technologies because of its stable, unique characteristics, low-price capture device, fast execution speed also it provides a large area for feature extraction Palmprint recognizes a person based on the principal lines, wrinkles and ridges on the surface of the palm The recognition process consists of image acquisition, preprocessing, feature extraction, matching and result The different techniques are used for the preprocessing, feature extraction, classifiers The methods discussed are for the online palmprint recognition

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

A Review of Sparse Recovery Algorithms

Abstract: Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, and data acquisition, the signals involved can be considered sparse or compressive in some domain. The compressive sensing theory could be a proper candidate to deal with these constraints. It can be used to recover sparse or compressive signals with fewer measurements than the traditional methods. Two problems must be addressed by compressive sensing theory: design of the measurement matrix and development of an efficient sparse recovery algorithm. These algorithms are usually classified into three categories: convex relaxation, non-convex optimization techniques, and greedy algorithms. This paper intends to supply a comprehensive study and a state-of-the-art review of these algorithms to researchers who wish to develop and use them. Moreover, a wide range of compressive sensing theory applications is summarized and some open research challenges are presented.

Effectiveness of artificial intelligence techniques against cyber security risks apply of IT industry

Abstract: The aim of the researcher was to determine the effectiveness of artificial intelligence techniques against cyber security risks particularly in case of Iraq, Researcher has opted for quantitative method of research design along with primary data. The researcher collected the data from employees working in this IT industry. The sample size for this study was 468 and confirmatory factor analysis, discriminant validity, basic analysis of model and lastly, hypothesis assessment was carried out. The P-values of all variables were obtained as significant apart from expert system which had no significant relation with artificial intelligence and cyber security. Geographical area, sample size, less variables and accessibility was the main issue.

Fingerprint Image Enhancement and Minutiae Extraction

Abstract: Automatic and reliable extraction of minutiae from fingerprint images is a critical step in fingerprint matching.The quality of input fingerprint images plays an important role in the performance of automatic identification and verification algorithms.Presents a fast fingerprint enhancement and minutiae extraction algorithm which improves the clarity of the ridge and valley structures of the input fingerprint images based on the frequency and orientation of the local ridges and thereby extracts correct minutiae.Experimental results show that the method performs well.

Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition

Abstract: CNNs have rapidly become state-of-the-art frameworks for various applications used in image classification. We typically need big, ground-based training data set so as to use revolutionary techniques in making a model more complex without affecting its accuracy and precision. In this case study, we shall discuss the different approaches and measures that are used in pattern recognition using CNN. We shall also discuss some of the major applications used nowadays in pattern recognition as well as their implementation (outputs only). We shall identify the key features used in building that application as well as its roles in making the application fit from any obstacles such as over-fitting, etc.