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33 results on '"Mantri, Dnyaneshwar"'

9. Occlusion and spoof attack detection using Haar Cascade classifier and local binary pattern for human face detection for ATM.

Author

Kulkarni, Nandkumar, Mantri, Dnyaneshwar, Pawar, Pranav, Deshmukh, Madhukar, and Prasad, Neeli

Subjects
*HUMAN skin color, *DEEP learning, *CONVOLUTIONAL neural networks, *IMAGE processing, *FEATURE extraction, *AUTOMATED teller machines, *CRIME statistics
Abstract

The crime rate has been rising at an unprecedented rate, and security has become a big concern in ATM machines. Face detection is the most common biometric technique due to its non-invasive nature. It's been used in a variety of fields, including camera auto focus, attendance, crowd monitoring, object tracking, security, system, etc. Face detection systems uses image processing techniques that are being learned to operate reliably in a variety of conditions, including changes in posture, lighting, skin color, occlusion, and face spoofing. The face detection system has become increasingly vulnerable to occlusion. Occlusion refers to the deliberate shielding of one's face with a helmet, sunglasses, scarves, or other items in order to avoid being caught. These issues have a significant impact on the development of image processing techniques and system's performance. In this paper the Haar Cascade Classifier (HCC) scheme is projected for face detection where precision as well as minimal processing time are important factors for ATM. The proposed scheme uses deep learning models such as Convolutional Neural Networks to enhance the reliability in feature extraction plus classification of images. Face biometric access control devices are becoming more common in everyday lives, but they remain vulnerable to spoofing attacks. This paper also proposes face spoofing identification using Local Binary Pattern (LBP) that has useful features for face detection. The proposed spoofing attack detection technique has yielded encouraging results. [ABSTRACT FROM AUTHOR]

Published
2022
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19. Synchronized Data Aggregation for Wireless Sensor Network

Author

Ramjee Prasad, Neeli R. Prasad, and Mantri Dnyaneshwar

Subjects
Computer science, business.industry, Wireless computing, business, Computer network
Abstract

Wireless Sensor Networks (WSNs) are used for monitoring and data collection purposes. A key challenge in effective data collection is to schedule and synchronize the activities of the nodes with global clock. This paper proposes the Synchronized Data Aggregation Algorithm (SDA) using spanning tree mechanism. It provides network-wide time synchronization for sensor network. In the initial stage algorithm established the hierarchical structure in the network and then perform the pair - wise synchronization. SDA aggregate data with a global time scale throughout the network. The aggregated packets are scheduled using TDMA as the MAC layer protocol. Simulation results show that, SDA gives promising result of energy efficiency and delay as compared with state-of-the-art solutions.

Published
2014

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