Face Detection Based on Depth Information Using HOG-LBP

Li, Tiemeng; Lyu, Fei; Lei, Yu; Xiao, Chunxia

doi:10.1109/imccc.2016.92

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The resource utilisation of each model was recorded and is provided in the Section 2.5.10. 11,12,13,14,15,16,17, and 18 describe the performance of each method on each dataset using cumulative error distribution curves, while Table 4 indicates the resources required to produce and test each model. Note that CPU utilisation is recorded as the average number of CPUs used over the course of executing the script.…”

Section: Methodsmentioning

confidence: 99%

“…An SVM classifier is then used to identify the face within the image based on the values of the histogram. This unique pixel gradient signature has proven to be useful in detecting faces in other image sources such as Li et al [17] who used camera depth information, as well as classifying if eyes are opened or closed within an image [18]. While HOG may no longer be considered state of the art, the method is still demonstrating its relevance and flexibility through its direct application in hardware.…”

Section: Stage 3: Regions Of Interestmentioning

confidence: 99%

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A review of image-based automatic facial landmark identification techniques

Johnston

Chazal

2018

J Image Video Proc.

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The accurate identification of landmarks within facial images is an important step in the completion of a number of higher-order computer vision tasks such as facial recognition and facial expression analysis. While being an intuitive and simple task for human vision, it has taken decades of research, an increase in the availability of quality data sets, and a dramatic improvement in computational processing power to achieve near-human accuracy in landmark localisation. The intent of this paper is to provide a review of the current facial landmarking literature, outlining the significant progress that has been made in the field from classical generative methods to more modern techniques such as sophisticated deep neural network architectures. This review considers a generalised facial landmarking problem and provides experimental examples for each stage in the process, reporting repeatable benchmarks across a number of publicly available datasets and linking the results of these examples to the recently reported performance in the literature.

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Section: Methodsmentioning

confidence: 99%

Section: Stage 3: Regions Of Interestmentioning

confidence: 99%

A review of image-based automatic facial landmark identification techniques

Johnston

Chazal

2018

J Image Video Proc.

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“…The face recognition is developed according to two main trends. Firstly, the studies focus on detecting human faces in images [1][2][3][4]. The face detector returns a rectangular bounding box containing a face.…”

Section: Introductionmentioning

confidence: 99%

Real-time eye ratio tracking for drowsiness detection based on machine vision and facial landmarks

2022

jsthaui

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Bài báo đề xuất thuật toán thời gian thực phát hiện hiện tượng ngủ gật dựa trên camera tiêu chuẩn. Các điểm mốc trên mặt được xác định, đặc biệt là tại vị trí xung quanh vùng mắt của đối tượng. Trên cơ sở đó, một hàm tỷ lệ về khoảng cách các điểm mốc vô hướng được tính toán đại diện cho thời gian mở và nhắm mắt theo thời gian thực. Ngoài ra, bài báo cũng tiến hành khảo sát độ tin cậy của giải pháp được sử dụng trong 3 trường hợp: cường độ ánh sáng thay đổi, đối tượng sử dụng kính, góc nghiêng của khuôn mặt thay đổi so với hướng quét của camera. Kết quả của bài báo có thể được ứng dụng trên các phương tiện giao thông để cảnh báo trạng thái ngủ gật của người điều khiển giúp điều khiển phương tiện an toàn hơn.

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“…But, since this method is based on single RGB (Red Green Blue) camera without depth information, it might lead to another problem. The face object on the camera could be a real face or a picture of a face printed on a magazine or a paper [3]. Hence, in this method traditional camera is not valid to ascertain the real face.…”

Section: Introductionmentioning

confidence: 99%

Face Detection and Recognition Using Haar Classifier and LBP Histogram

Das¹,

Gangopadhyay²,

Chatterjee³

2018

ijarcs

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Abstract:Facial recognition technology is the process for identifying or verifying a face from digital images. The need for face recognition has been of real importance with the development of modern society. Detection and recognition of faces has been on the rise worldwide owing the requirement for security for economic transactions, authorization, national safety and security and other important factors. The technology comprises of face detection, database creation and face recognition. This paper presents a new approach of face identification using LBP method and Haar-like features. The first step is face detection which is done using Haar cascade classifier. After detection, a face is saved in the database. Then the faces from the database are passed through the face recognition algorithm. The Local Binary Pattern Histogram (LBPH) method is used for face recognition. The performance of face detection can be seen to produce maximum error of 1.6%, 2.1% and 0.8% in case of Real-Time video, image file and video file respectively which may be considered accurate. The recognition algorithm produces maximum error of 0.4% which may be considered accurate as well.

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Face Detection Based on Depth Information Using HOG-LBP

Cited by 9 publications

References 8 publications

A review of image-based automatic facial landmark identification techniques

A review of image-based automatic facial landmark identification techniques

Real-time eye ratio tracking for drowsiness detection based on machine vision and facial landmarks

Face Detection and Recognition Using Haar Classifier and LBP Histogram

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