Optimization of the Data Representation Integrated Form in the Viola-Jones Algorithm for a Person’s Face Search

Alyushin, Mikhail V.; Alyushin, Victor; Kolobashkina, Lyubov V.

doi:10.1016/j.procs.2018.01.004

Cited by 19 publications

(11 citation statements)

References 4 publications

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“…First phase: identifying individual tubers in the image. Because the system must process 4-5 images with a resolution of 3840 × 2160 pixels per second, the Viola-Jones method [58][59][60][61][62][63] is the most suitable method for detecting tubers. But even taking into account the high performance of this algorithm, the processing of such an image format will take an unacceptably long time.…”

Section: Resultsmentioning

confidence: 99%

“…We proposed using the Viola-Jones algorithm at the first stage of processing the image from a video camera, which, unlike convolutional neural networks, works in a real-time mode [58][59][60][61][62][63]. This method was created for recognizing human faces and did not give good results when used to detect potato tubers; however, by selecting preprocessing filters, we achieved a probability of 97%, which corresponds to the results of a convolutional neural network (from 91 to 95% in works on convolutional networks for the last three years) [25][26][27][28][29][30][31].…”

Section: Discussionmentioning

confidence: 99%

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Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

et al. 2021

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The article discusses the problem of detecting sick or mechanically damaged potatoes using machine learning methods. We proposed an algorithm and developed a system for the rapid detection of damaged tubers. The system can be installed on a conveyor belt in a vegetable store, and it consists of a laptop computer and an action camera, synchronized with a flashlight system. The algorithm consists of two phases. The first phase uses the Viola-Jones algorithm, applied to the filtered action camera image, so it aims to detect separate potato tubers on the conveyor belt. The second phase is the application of a method that we choose based on video capturing conditions. To isolate potatoes infected with certain types of diseases (dry rot, for example), we use the Scale Invariant Feature Transform (SIFT)—Support Vector Machine (SVM) method. In case of inconsistent or weak lighting, the histogram of oriented gradients (HOG)—Bag-of-Visual-Words (BOVW)—neural network (BPNN) method is used. Otherwise, Otsu’s threshold binarization—a convolutional neural network (CNN) method is used. The first phase’s result depends on the conveyor’s speed, the density of tubers on the conveyor, and the accuracy of the video system. With the optimal setting, the result reaches 97%. The second phase’s outcome depends on the method and varies from 80% to 97%. When evaluating the performance of the system, it was found that it allows to detect and classify up to 100 tubers in one second, which significantly exceeds the performance of most similar systems.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

et al. 2021

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“…On the other hand, to recognise the face areas inside this photo, Hong et al [10] employed a Viola-Jones approach that relies upon Haar-like features. Alyushin et al [11] subsequently enhanced this technique by altering the built-in image frame forms to detect the face of the person while headache angles are rotated.…”

Section: Related Studies Of Face Recognitionmentioning

confidence: 99%

Single and Multiface Detection and Recognition System

Ariff

Jaafar

Jusoh

et al. 2022

J. Phys.: Conf. Ser.

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Face detection has drawn the interest of numerous research groups because to its vast application in various domains such as surveillance and security systems, as human-computer interaction, and many more. Face identification is the important phase involving several factors such as lighting, facial expression, and ageing effects. It’s more tough as detection takes a lot of time to detect and distinguish a single face at a time. Moreover, most of the existing technology cannot accurately detect many faces simultaneously. This study therefore presents a system that can recognize and identify multiple face image simultaneously with various expressions. Face-recognition procedure consists of data gathering, face detection, extraction, and classification feature. The face dataset is obtained from 10 participants with varied backgrounds and expressions. Subsequently, the viola-jones technique together with threshold technique is utilized in face detection to detect face presents while removing the unnecessary background to reduce face recognition time processing further. The Principal Component Analysis (PCA) is then employed to extract features while maintaining as much information as possible from enormous image data set. After formulating each face’s representation, the classification process is considered to recognize the identities of users’ faces. Here, a non-parametric classifier i.e. Support Vector Machine (SVM) is applied in this process. Conclusively, the system is able to detect around 90 percent multi-face user in different conditions.

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“…and Sw within-class scatter matrix is shown as follows: (4) where if N present the total of data and C present the total of class, then NC present the total of data in class C. Meanwhile, x is the vector and μ is the mean of the vector.…”

Section: Fisher Facementioning

confidence: 99%

“…Despite image integral can be calculated quickly, the representation of integral is less responsive to changes in face angle [2] [3] [4]. These problems decrease the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition using Fisher Face-based Viola-Jones Algorithm

Kirana¹,

Wibawanto²,

Herwanto³

2018

EECSI

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In the form of the image integral, this primitive feature accelerates the performance of the Viola-Jones algorithm. However, the robust feature is necessary to optimize the results of emotion recognition. Previous research [11] has shown that fisher face optimized projection matrix in the low dimensional features. This feature reduction approach is expected to balance time-consuming and accuracy. Thus we proposed emotion recognition using fisher face-based Viola-Jones Algorithm. In this study, PCA and LDA are extracted to get the fisher face value. Then fisher face is filtered using Cascading AdaBoost algorithm to obtain face area. In the facial area, the Cascading AdaBoost algorithm re-employed to recognize emotions. We compared the performance of the original viola jones and fisher face-based viola jones using 50 images on the State University of Malang dataset by measuring the accuracy and time-consuming in the fps. The accuracy and time-consuming of the Viola-Jones algorithm reach 0.78 and 15 fps, whereas our proposed methods reach 0.82 and 1 fps. It can conclude that the fisher face-based viola-jones algorithm recognizes facial emotion as more accurate than the viola-jones algorithm.

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Optimization of the Data Representation Integrated Form in the Viola-Jones Algorithm for a Person’s Face Search

Cited by 19 publications

References 4 publications

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems

Single and Multiface Detection and Recognition System

Emotion Recognition using Fisher Face-based Viola-Jones Algorithm

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