Face detection approach from video with the aid of KPCM and improved neural network classifier

Yoganand, A. Vivek; Kavida, A. Celine; Rukmanidevi,

doi:10.1007/s11042-018-6191-2

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The performance of the proposed CNN-mLSTM is evaluated in this section, and the performance results are compared with existing CNN [7], DP-Adaboost [9] and improved neural network [11] face detection schemes. The facts given below show that the device proposed has achieved better performance in terms of precision, fmeasurement, recall and accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…The architecture in cascading with three phases carefully designed [10] to forecast the presence of faces, based on deep convolutional networks. In [11] an important facial recognition system has been developed to index a specific face from various video images. The classifier used is the enhanced neural grid that optimizes weight factors with the modified cuckoo search algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method

Parikibanda¹

2020

J Comput Sci Intell Technol

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For real-world applications, such as video monitoring, interaction between human machines and safety systems, face recognition is very critical. Deep learning approaches have demonstrated better results in terms of precision and processing speed in image recognition compared to conventional methods. In comparison to traditional methods. While facial detection problems with different commercial applications have been extensively studied for several decades, they still face problems with many specific scenarios, due to various problems such as severe facial occlusions, very low resolutions, intense lighting and exceptional changes in image or video compression artifacts, etc. The aim of this work is to robustly solve the issues listed above with a facial detection approach called Convolution Neural Network with Long short-term Model (CNN-mLSTM). This method first flattened the original frame, calculating the gradient image with Gaussian filter. The edge detection algorithm Canny-Kirsch Method will then be used to identify edge of the human face. The experimental findings suggest that the technique proposed exceeds the current modern methods of face detection.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method

Parikibanda¹

2020

J Comput Sci Intell Technol

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“…In addition, to fulfill the requirements of online detection, the proposed approach incorporates an approximator. Authors use the frame-level precision–recall curve (PRC) and corresponding area under the curve (average precision, AP) ( Wu et al., 2020a ; Wu et al., 2020b ) instead of the receiver operating characteristic curve (ROC) and corresponding AUC ( Yoganand & Kavida, 2018 ; Xie et al, 2016 ) because AUC typically shows an optimistic result when dealing with class-imbalanced data, whereas PRC and AP focus on positive samples (violence). The proposed approach beats other state-of-the-art algorithms in the publicly available dataset created by authors.…”

Section: Classification Of Violence Detection Techniquesmentioning

confidence: 99%

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

Omarov

Narynov²,

Zhumanov

et al. 2022

PeerJ Computer Science

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We investigate and analyze methods to violence detection in this study to completely disassemble the present condition and anticipate the emerging trends of violence discovery research. In this systematic review, we provide a comprehensive assessment of the video violence detection problems that have been described in state-of-the-art researches. This work aims to address the problems as state-of-the-art methods in video violence detection, datasets to develop and train real-time video violence detection frameworks, discuss and identify open issues in the given problem. In this study, we analyzed 80 research papers that have been selected from 154 research papers after identification, screening, and eligibility phases. As the research sources, we used five digital libraries and three high ranked computer vision conferences that were published between 2015 and 2021. We begin by briefly introducing core idea and problems of video-based violence detection; after that, we divided current techniques into three categories based on their methodologies: conventional methods, end-to-end deep learning-based methods, and machine learning-based methods. Finally, we present public datasets for testing video based violence detectionmethods’ performance and compare their results. In addition, we summarize the open issues in violence detection in videoand evaluate its future tendencies.

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“…Between layers, neurons are fully connected. Except for the input layer, the input of each layer is closely related to the output of the previous layer [3][4]. Generally speaking, BP neural network is a kind of negative feedback neural network.…”

Section: Introductionmentioning

confidence: 99%

Chinese Text Event Detection Technology Based on Improved Neural Network

Cui¹,

Wei²

2021

Frontiers in Artificial Intelligence and Applications

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With the development of deep learning, the use of neural network for text detection has been more in-depth research and more widely used. Based on this, this paper studies the Chinese text event detection technology based on improved neural network. In the research, this paper uses the flower pollination algorithm (FPA) to improve the traditional BP neural network algorithm. By optimizing the weights and thresholds of BP neural network, a Chinese text event detection method based on improved neural network is proposed. In order to verify the effect of the Chinese text event detection method based on improved neural network, this paper compares it with the natural scene text detection method, and compares the recall rate, accuracy rate and time-consuming. The results show that the accuracy rate of the natural scene text detection method is 88%, and the recall rate is 73%. The accuracy rate of the text detection method based on the improved neural network is 95% and the recall rate is 86%. The F value of the natural scene text detection method in the Chinese text event detection test is 0.79, which takes 4.56s The value of F in is 0.90, which takes 0.64s. Therefore, the Chinese text event detection method based on improved neural network has better performance.

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Face detection approach from video with the aid of KPCM and improved neural network classifier

Cited by 6 publications

References 13 publications

Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method

Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

Chinese Text Event Detection Technology Based on Improved Neural Network

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