Hardware Implementation of Real-Time, High Performance, RCE-NN Based Face Recognition System

Sardar, Santu; Babu, K. Ananda

doi:10.1109/vlsid.2014.37

Cited by 17 publications

(10 citation statements)

References 17 publications

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“…Our recognition system has higher values for all of the hardware parameters, but it has three main benefits: (i) performing the recognition process in two stages, which reduces the possibility of passing and accepting infected biometric data; (ii) performing a filtering process to eliminate the impact of a Hardware Trojan if applicable; and (iii) easier to locate an intrusion in the system due to a decussate and chaining hardware-software architecture. Meanwhile, our system has a conspicuously smaller hardware footprint in comparison to the existing exclusively hardware-based works, and its algorithm is computationally much simpler in comparison to the existing lightweight biometric recognition systems [12][13][14][15][16][17][18][19][20][21][22]. …”

Section: Resultsmentioning

confidence: 99%

A Cross-Layer Biometric Recognition System for Mobile IoT Devices

Taheri

Yuan

2018

Electronics

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A biometric recognition system is one of the leading candidates for the current and the next generation of smart visual systems. The visual system is the engine of the surveillance cameras that have great importance for intelligence and security purposes. These surveillance devices can be a target of adversaries for accomplishing various malicious scenarios such as disabling the camera in critical times or the lack of recognition of a criminal. In this work, we propose a cross-layer biometric recognition system that has small computational complexity and is suitable for mobile Internet of Things (IoT) devices. Furthermore, due to the involvement of both hardware and software in realizing this system in a decussate and chaining structure, it is easier to locate and provide alternative paths for the system flow in the case of an attack. For security analysis of this system, one of the elements of this system named the advanced encryption standard (AES) is infected by four different Hardware Trojansthat target different parts of this module. The purpose of these Trojans is to sabotage the biometric data that are under process by the biometric recognition system. All of the software and the hardware modules of this system are implemented using MATLAB and Verilog HDL, respectively. According to the performance evaluation results, the system shows an acceptable performance in recognizing healthy biometric data. It is able to detect the infected data, as well. With respect to its hardware results, the system may not contribute significantly to the hardware design parameters of a surveillance camera considering all the hardware elements within the device.

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Section: Resultsmentioning

confidence: 99%

A Cross-Layer Biometric Recognition System for Mobile IoT Devices

Taheri

Yuan

2018

Electronics

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“…The existing technique presented by Sardar and Babu [20] uses 39432 slice registers and the maximum operating frequency is 80MHz.…”

Section: ) Performance Analysis Using Hardware Comparison Resultsmentioning

confidence: 99%

An Adaptive Threshold based FPGA Implementation for Object and Face detection

Kumar

Sarkar

Bhairannawar

et al. 2015

2015 Third International Conference on Image Information Processing (ICIIP)

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“…The percentage values of TSR of proposed method is compared with existing methods presented by Ben Niu et al, [11], Sardar and Babu [32], Junjie Yan et al, [33], Thiago H.H. Zavaschi et al, [34], Manchula and Arumugam [35] and Wang and Yin [36].…”

Section: Performance Comparisons Of Proposed Techniques and Existing mentioning

confidence: 99%

FPGA Implementation of Moving Object and Face Detection Using Adaptive Threshold

H.C¹,

Sarkar²,

Bhairannawar³

et al. 2015

VLSICS

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The real time moving object and face detections are used for various security applications. In this paper, we propose FPGA implementation of moving object and face detection with adaptive threshold. The input images are passed through Gaussian filter. The 2D-DWT is applied on Gaussian filter output and considered only LL band for further processing to detect object/face. The modified background subtraction technique is applied on LL bands of input images. The adaptive threshold is computed using LL-band of reference image and object is detected through modified background subtraction. The detected object is passed through Gaussian filter to get final good quality object. The face detection is also identified using matching unit along with object detection unit. The reference image is replaced by face database images in the face detection. It is observed that the performance parameters such as TSR, FRR, FAR and hardware related results are improved compared to existing techniques.

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Hardware Implementation of Real-Time, High Performance, RCE-NN Based Face Recognition System

Cited by 17 publications

References 17 publications

A Cross-Layer Biometric Recognition System for Mobile IoT Devices

A Cross-Layer Biometric Recognition System for Mobile IoT Devices

An Adaptive Threshold based FPGA Implementation for Object and Face detection

FPGA Implementation of Moving Object and Face Detection Using Adaptive Threshold

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