Lightweight mobile object recognition

Czúni, László; Kiss, Péter József; Lipovits, Ágnes; Gál, Mónika

doi:10.1109/icip.2014.7025695

Cited by 7 publications

(2 citation statements)

References 11 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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“…In [ 25 ], it is shown that the color and edge directivity descriptor (CEDD) [ 26 ] is a robust visual tool and can be computed quickly in mobile platforms. The CEDD is an area-based descriptor where pixels are classified into one of six texture classes (non-edge, vertical, horizontal, 45 and 135 degree diagonal, and non-directional edges) with the help of the MPEG-7 Edge Histogram Descriptor.…”

Section: Viewer-centered Visual Representation With Orientation Damentioning

confidence: 99%

Lightweight Active Object Retrieval with Weak Classifiers

Czúni

Rashad

2018

Sensors

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In the last few years, there has been a steadily growing interest in autonomous vehicles and robotic systems. While many of these agents are expected to have limited resources, these systems should be able to dynamically interact with other objects in their environment. We present an approach where lightweight sensory and processing techniques, requiring very limited memory and processing power, can be successfully applied to the task of object retrieval using sensors of different modalities. We use the Hough framework to fuse optical and orientation information of the different views of the objects. In the presented spatio-temporal perception technique, we apply active vision, where, based on the analysis of initial measurements, the direction of the next view is determined to increase the hit-rate of retrieval. The performance of the proposed methods is shown on three datasets loaded with heavy noise.

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The use of IMUs for video object retrieval in lightweight devices

Czúni

Rashad

2017

Journal of Visual Communication and Image Representation

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Lightweight mobile object recognition

Cited by 7 publications

References 11 publications

A Cross-Layer Biometric Recognition System for Mobile IoT Devices

A Cross-Layer Biometric Recognition System for Mobile IoT Devices

Lightweight Active Object Retrieval with Weak Classifiers

The use of IMUs for video object retrieval in lightweight devices

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