Face Detection Approach in Neural Network Based Method for Video Surveillance

Bojković, Zoran; Samcovic, Andreja

doi:10.1109/neurel.2006.341172

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…According to the diagram as illustrated in Figure 7 that describes the detection rate level of diverse Artificial Neural Network approaches for many of previous research studies, we can perceive that the lowest detection rate is obtained from the use of the HNN approach (Lin-Lin H et al, 2003). Despite, the graph shows that the highest detection level can be gained through the adaption of the CNN approach (Matsugu et al, 2003), and BPNN that was utilized in (Bojkovic & Samcovic, 2006) also acquire a good detection rate level. Furthermore, it was observed that each of these research papers have specific limitations as well as specific strengths over other topologies (Kaushal & Raina, 2010;Bouzerdoum, 2000).…”

Section: Resultsmentioning

confidence: 98%

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Face Recognition Based on Artificial Neural Network: A Review

Alblushi

2021

airdj

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The face recognition/detection is considered as one of the most popular applications in the field of image processing and biometric pattern recognition systems. Although the face recognition approach improves authentication procedure, nevertheless still many challenges appear due to diversities in human facial expression, image huge size, background complexity, variation in illumination, poses, blurry, etc. Therefore, the face detection procedure is classified as one of the most difficult tasks in computer vision. This research paper tends to address the concept of image processing along with the use of the Artificial Neural Network approach and represent it is a potential capability in enhancing the method of extracting face pattern through an adaption of various ANN topologies. Furthermore, it represents fundamental phases associated with the construction of any facial recognition system. Finally, it provides a general overview of different literature survives that related to face recognition based on the use of different ANN approaches and algorithms

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Section: Resultsmentioning

confidence: 98%

“…The experiment includes 2900 fragment of facial images that were used in the training. The results show that the proposed system achieves 97.6% as the correct recognition rate Bojkovic & Samcovic (2006),. developed face recognition system for surveillance based on the use of ANN and Multilayered Back Propagation Neural Network (BPNN).…”

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confidence: 92%

Face Recognition Based on Artificial Neural Network: A Review

Alblushi

2021

airdj

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“…Different models of ANN have been used for face recognition, and reason for that is ability of those models to resemble the way in which neurons function in human brain. Researches [8], [9] have been using ANN for face detection and recognition on video stream. Common problem is distinction between different skin colors.…”

Section: Eai Endorsed Transactions On Industrial Network and Intellimentioning

confidence: 99%

Comparison of Different Neural Network Training Algorithms with Application to Face Recognition

Vulović¹,

Šušteršič²,

Peulić³

et al. 2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Research in the field of face recognition has been popular for several decades. With advances in technology, approaches to solving this problems haves changed. Main goal of this paper was to compare different training algorithms for neural networks and to apply them for face recognition as it is a nonlinear problem. Algorithm that we have used for face recognition problem was the Eigenface algorithm that belongs to the Principal Component Analysis (PCA) algorithms. Percentage of recognition for all the used training functions is above 90%.

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“…Another disadvantage with the IEEE 802.11 is that it does not offer any guarantee on quality of service (i.e., throughput, delay jitter) and hence, is not suitable for video data transmission. Other video surveillance technologies reported in the literature, are either for static environment [11][12][13] (e.g., intelligent analysis of CCTV coverage at train stations/airports) or for very low mobility environment [14] (e.g., robot vision in underground mining). To the best our knowledge, there is no suitable technology that can be readily used to facilitate video transmission from a vehicle moving at high vehicular speeds.…”

Section: Introductionmentioning

confidence: 99%

A WiMAX Solution For Real-Time Video Surveillance in Public Transport

Ahmad

Habibi²

2011

IJCNC

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Video surveillance is a highly useful tool to the public transport authorities, which is now widely used as a measure to ensure passenger safety and security. While video surveillance application in static environments like airports, shopping malls, train stations has been a huge success, real-time video surveillance in moving public transport experiences serious technological challenges mainly due to low data rates at vehicular speeds offered by existing communication technologies. Success of live video surveillance in public transport depends on future communication technologies. WiMAX has emerged as an exciting technology with promises to offer high throughput, a key requirement for video surveillance in public transport. WiMAX, however, offers limited throughput at high vehicular speeds mainly because of multipath fading that causes high bit error rate at the receiver. In this paper, we propose a forward error control (FEC) scheme for WiMAX that proactively and dynamically adjusts the size of extra parity bits of error correcting codes for real-time applications like video surveillance based on the estimated bit error probability at various vehicular speeds. We further propose a model to improve the utility gain of a live video surveillance system in public transport that uses WiMAX technology. Simulation results confirm that the proposed scheme significantly improves the throughput and utility of the video surveillance system in public transport.

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Face Detection Approach in Neural Network Based Method for Video Surveillance

Cited by 10 publications

References 9 publications

Face Recognition Based on Artificial Neural Network: A Review

Face Recognition Based on Artificial Neural Network: A Review

Comparison of Different Neural Network Training Algorithms with Application to Face Recognition

A WiMAX Solution For Real-Time Video Surveillance in Public Transport

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