Variational Feature Representation-based Classification for face recognition with single sample per person

Ding, Ruxi; Du, Daniel K.; Huang, Zheng-Hai; Li, Zhiming; Shang, Ke

doi:10.1016/j.jvcir.2015.03.001

Cited by 25 publications

(4 citation statements)

References 38 publications

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“…The first category is featurerepresentation-based methods, which mainly focus on the feature extraction process. One application of this method is to conduct single sample per person (SSPP) classification (Ding et al 2015). Shao et al (2003) introduced a hyper-polyhedron with the adaptive threshold (HPAT) technique, which can be used for small building images.…”

Section: Image-based Methods and Automatic Recognitionmentioning

confidence: 99%

Improvement of the inspection-repair process with building information modelling and image classification

Zhan

Huang

et al. 2019

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Purpose-Automated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focused on automation of visual information, such as images, when improving timely maintenance decisions. This study aimed to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM). Design/methodology/approach-To improve and automate the inspection-repair process, image classification algorithms were employed to connect images with a corresponding image database in a BIM knowledge repository. QR code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system. Findings-The system developed in this study associates the inspection-repair process with a digital three dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with decision-making. Originality/value-This study introduces an innovative approach that applies image classification and leverages a BIM knowledge repository to enhance the inspection-repair process in facility management practice. The system designed provides automated image-classifying data from a smart phone, eliminates time required to input image data manually, and improves communication and collaboration between FM personnel for maintenance in the decision-making process.

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Section: Image-based Methods and Automatic Recognitionmentioning

confidence: 99%

Improvement of the inspection-repair process with building information modelling and image classification

Zhan

Huang

et al. 2019

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“…Finally, based on the features computed, the classifier categorizes the face in the appropriate class image. However, face recognition suffers from some challenges such as pose variations, feature occlusion, facial expressions and lightening conditions 4,5 …”

Section: Introductionmentioning

confidence: 99%

Principal component analysis, hidden Markov model, and artificial neural network inspired techniques to recognize faces

Aggarwal¹,

Alshehri

Kumar

et al. 2020

Concurrency and Computation

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Face Recognition is a challenging task for recognizing and detecting the identity of an individual. Although, plethora of work has already been done in the field of pattern recognition still there has been lot which has not been addressed in any of the literature. In the current research, we have presented a comparative analysis using three popularly known techniques for face recognition namely, Principal Components Analysis (PCA) using Eigen Faces, Hidden Markov Model (HMM) using Singular Value Decomposition, and Artificial Neural Network (ANN) using Gabor filters. These techniques are implemented and evaluated using various measuring metrics such as false acceptance, false recognition rate, and so on. We used ORL and Yale Face dataset to test the robustness of implemented algorithms. Results show that ANN model for face recognition outperforms the other two techniques by achieving more accurate results and shows the highest recognition rate of 97.49% on ORL database. Moreover, it is also observed that ANN model shows the minimum error count of about 2.502% on ORL database while it is 3.5% on Yale Face dataset. To evaluate further, the implemented techniques are compared with best known techniques in class implemented by various researchers.

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“…To address parts of this matter, several studies suggested adopting facial recognition to accelerate automatic search of a required face (7). The recent advances in the field have recently elevates the problems and successfully to an extent resolves a Single Sample per Person (SSPP) issue where only one photo per person can be collected (8,9,10). It should be noted that, those algorithms cannot operate in the absence of a digital face to compare with.…”

Section: Introductionmentioning

confidence: 99%

Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model

Horkaew¹

2020

Baghdad Sci.J

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Facial recognition has been an active field of imaging science. With the recent progresses in computer vision development, it is extensively applied in various areas, especially in law enforcement and security. Human face is a viable biometric that could be effectively used in both identification and verification. Thus far, regardless of a facial model and relevant metrics employed, its main shortcoming is that it requires a facial image, against which comparison is made. Therefore, closed circuit televisions and a facial database are always needed in an operational system. For the last few decades, unfortunately, we have experienced an emergence of asymmetric warfare, where acts of terrorism are often committed in secluded area with no camera installed and possibly by persons whose photos have never been kept in any official database prior to the event. During subsequent investigations, the authorities thus had to rely on traumatized and frustrated witnesses, whose testimonial accounts regarding suspect’s appearance are dubious and often misleading. To address this issue, this paper presents an application of a statistical appearance model of human face in assisting suspect identification based on witness’s visual recollection. An online prototype system was implemented to demonstrate its core functionalities. Both visual and numerical assessments reported herein evidentially indicated potential benefits of the system for the intended purpose.

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Variational Feature Representation-based Classification for face recognition with single sample per person

Cited by 25 publications

References 38 publications

Improvement of the inspection-repair process with building information modelling and image classification

Improvement of the inspection-repair process with building information modelling and image classification

Principal component analysis, hidden Markov model, and artificial neural network inspired techniques to recognize faces

Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model

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