Reliable face detection in varying illumination and complex background

Powar, Varsha; Jahagirdar, Aditi

doi:10.1109/iccict.2012.6398185

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Misses is the errors of describing a facial expression as being absent when it was present. Studies indicate that the pattern of confusion becomes worse when some other challenges occur at the same time, such as illumination or occlusion in an image [153].…”

Section: Real-world Spontaneous Data Collectionmentioning

confidence: 99%

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Pourebadi

Riek

2022

ACM Trans. Comput. Healthcare

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Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.

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Section: Real-world Spontaneous Data Collectionmentioning

confidence: 99%

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Pourebadi

Riek

2022

ACM Trans. Comput. Healthcare

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“…Research shows that this phenomenon happens in most of the face detection methods, and this problem becomes worse when some other challenges occur at the same time, such as illumination or occlusion in an image [16]. The problem is illustrated in Figure 2, which shows the correct and incorrect selected faces in an image.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection

Sharifara

Rahim

Navabifar

et al. 2017

Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments

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Face detection is one of the challenging tasks in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as face recognition, face tracking, image database management, etc. In these applications, face objects often come from an inconsequential part of images that contain variations, namely different illumination, poses, and occlusion. These variations can decrease face detection rate noticeably. Most existing face detection approaches are not accurate, as they have not been able to resolve unstructured images due to large appearance variations and can only detect human faces under one particular variation. Existing frameworks of face detection need enhancements to detect human faces under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework is proposed to improve detection rate based on skin color and provide a validation process. A preliminary segmentation of the input images based on skin color can significantly reduce search space and accelerate the process of human face detection. The primary detection is based on Haar-like features and the Adaboost algorithm. A validation process is introduced to reject non-face objects, which might occur during the face detection process. The validation process is based on two-stage Extended Local Binary Patterns. The experimental results on the CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. CCS Concepts • Computing methodologies~Computer vision• Computing methodologies~Machine learning algorithms • Computing methodologies~Image manipulation • Theory of computation~Design and analysis of algorithms

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“…Finally it is combined with edges of the color image. Powar et al [4] adopted the so called "reference white" to realize light compensation and detect the candidate face region by the YCbCr skin color model. Then they used the morphological operations and Adaboost algorithm to accomplish the accurate face detection.…”

Section: Introductionmentioning

confidence: 99%

Illumination Variant Face Detection System Using Hierarchical Feature Method

Hsia

Chiang

Lin

2015

AMM

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In this study, we propose a new solution based on Adaboost algorithm and Back Propagation Network (BPN) of Neural Network (NN) combining local and global features with cascade architecture to detect human faces. We use Modified Census Transform (MCT) feature that belong to texture features and is less sensitive to illumination for local feature calculation. By this approach, it is not necessary to preprocess each sub-window of the image. For classification, we use the structure of hierarchical feature to control the number of features. With only MCT, it is easy to misjudge faces, and therefore in this work we include the brightness information of global features to eliminate the false positive regions. As a result, the proposed approach can have Detection Rate (DR) of 99%, false positives of only 11, and detection speed of 27.92 Frame Per Second (FPS).

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Reliable face detection in varying illumination and complex background

Cited by 5 publications

References 3 publications

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection

Illumination Variant Face Detection System Using Hierarchical Feature Method

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