Multi-View Faces Detection Using Viola-Jones Method

Winarno, Edy; Hadikurniawati, Wiwien; Nirwanto, Ahmad Ainun; Abdullah, Dahlan

doi:10.1088/1742-6596/1114/1/012068

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…When a face is detected, a rectangular contour is drawn around the face. The value of the characteristic is the difference between the black and white regions [15]. However, the total number of Haar features is very large, much compared to the number of pixels; AdaBoost is used to select the specific Haar function used and set the threshold value.…”

Section: A Methodsmentioning

confidence: 99%

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Video Analysis with Faces using Harris Detector and Correlation

Herrera¹,

Gallegos-Funes²,

Martínez³

2019

IJACSA

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A procedure is presented to detect changes in a video sequence based on the Viola & Jones Method to obtain images of faces of persons; videos are taken of the web. The software allows to obtain images or frames separated from nose, mouth, and eyes but the case of the eyes is taken as an example. Change detection is done by using correlation and the Harris detector. The correlation results allow us to recognize changes in position in the person or movement of the camera if the individual remains fixed and vice versa. It is possible to analyze a part of the face thanks to the Harris detector; It is possible with detected reference points to recognize very small changes in the video sequence of a particular organ; such as the human eye, even when the image is of poor quality as is the case of videos downloaded from the Internet or taken with a low-resolution camera.

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Section: A Methodsmentioning

confidence: 99%

“…In this investigation, the Viola & Jones method is used for the location of the face in video sequences and the segmentation in RGB for its follow-up [11] [12]. Subsequently, a probabilistic analysis is used to see the relationship between one image and another and detect changes [13].…”

Section: Introductionmentioning

confidence: 99%

Video Analysis with Faces using Harris Detector and Correlation

Herrera¹,

Gallegos-Funes²,

Martínez³

2019

IJACSA

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“…In comparison with FACE API, the speed of the local detection is 4 times and 20 times via network, but can handle a rotation of 45 • . Winarno et al [115] built face counter to count faces in an image, using the Viola-Jones algorithm. The model resulted in poor accuracy in images with low light intensity.…”

Section: Viola-jones Algorithmmentioning

confidence: 99%

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

et al. 2021

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Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.

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“…Region identification in the extraction of breathing signals was therefore further examined by implementing a facial recognition and nose identification algorithm. character vector, which in this thesis was [15 25], representing a subject's mouth [127]. An example of a detected mouth in a thermal image complement can be seen in Figure 5.19.…”

Section: Respiration Measurementsmentioning

confidence: 99%

“…Method 2 processing began by applying the Viola-Jones algorithm to each smoothed, enhanced thermal image. The Viola-Jones algorithm is used to find facial features in a grayscale visible-light image, which has different topographical properties than a grayscale thermal image[127]. However, when examining a subject's mouth, a visible-light image and thermal image complement have similar topographical properties.…”

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confidence: 99%

Non-Contact Bed-Based Monitoring of Vital Signs

Bennett¹

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Vital signs are a set of commonly measured signals used internationally as a baseline in medicine and surgery and are one of the most accurate predictors of clinical and physiological deterioration. Despite the clear clinical importance of vital sign measurements, there is often missed or inadequate documentation of patient vital sign measurements. The development of unobtrusive, automated and continuous monitoring offers the potential to enhance the safety and quality of patient care. This thesis details a system that uses multiple modalities to capture data and data processing techniques to extract vital signal measurements and vital signal measurement abnormalities related to subject morbidities. This thesis focuses on the examination, testing and improvement upon a current visible light video processing technique intended to extract vital signal measurements, and expand it's use to thermal infrared video vital signal extraction. Three modalities were used to gather data from healthy adult subjects and older adult inhospital patients: thermal infrared cameras, visible light cameras and pressure sensitive mats. Subjects participated in several experimental procedures including video data capture of faces, hands and feet as well as in-bed pressure mat data capture. This data was subjected to several stages of data processing to extract vital signal measurements, which include pulse, respiration temperature and mobility measurements. Data segmentation using binary masks, level set method, and watershed method were used to identify regions of interest. An adaptive spatio-temporal video processing algorithm, the main thesis contribution, was used to extract vital signal measurements. The developed algorithm was assessed for its performance in vital signal estimation, as well its

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Multi-View Faces Detection Using Viola-Jones Method

Cited by 14 publications

References 6 publications

Video Analysis with Faces using Harris Detector and Correlation

Video Analysis with Faces using Harris Detector and Correlation

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

Non-Contact Bed-Based Monitoring of Vital Signs

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