Intelligent Sensor for Image Control Point of Eigenfaces for Face Recognition

Touré, Mohamed Lamine; Zou, Beiji

doi:10.3844/jcssp.2010.484.491

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…In related research, Mohamed et al used the Eigenface technique to attempt to match the input face images with face images in the database with known identity [14]. The system mainly involved three parts, i.e., Generating Eigenfaces, Face Classification and Face Identification.…”

Section: Introductionmentioning

confidence: 99%

Eigenface Algorithm-Based Facial Expression Recognition in Conversations - An Experimental Study

Fei

Yang

et al. 2018

Advances in Brain Inspired Cognitive Systems

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Recognizing facial expressions is important in many fields such as computer-human interface. Though different approaches have been widely used in facial expression recognition systems, there are still many problems in practice to achieve the best implementation outcomes. Most systems are tested via the lab-based facial expressions, which may be unnatural. Particularly many systems have problems when they are used for recognizing the facial expressions being used during conversation. This paper mainly conducts an experimental study on Eigenface algorithm-based facial expression recognition. It primarily aims to investigate the performance of both lab-based facial expressions and facial expressions used during conversation. The experiment also aims to probe the problems arising from the recognition of facial expression in conversations. The study is carried out using both the author's facial expressions as the basis for the lab-based expressions and the facial expressions from one elderly person during conversation. The experiment showed a good result in lab-based facial expressions, but there are some issues observed when using the case of facial expressions obtained in conversation. By analyzing the experimental results, future research focus has been highlighted as the investigation of how to recognize special emotions such as a wry smile and how to deal with the interferences in the lower part of face when speaking.

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Section: Introductionmentioning

confidence: 99%

Eigenface Algorithm-Based Facial Expression Recognition in Conversations - An Experimental Study

Fei

Yang

et al. 2018

Advances in Brain Inspired Cognitive Systems

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“…Case three typically shows up as a false positive in most recognition systems. In this framework, however, the false recognition may be detected because of the significant distance between the image and the subspace of expected face images (Toure and Beiji, 2010). …”

Section: Extracting Eigen Features F1mentioning

confidence: 99%

Improving the Performance of Machine Learning Based Multi Attribute Face Recognition Algorithm Using Wavelet Based Image Decomposition Technique

Sakthivel¹,

Rajaram²

2011

Journal of Computer Science

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Problem statement: Recognizing a face based attributes is an easy task for a human to perform; it is closely automated and requires little mental effort. A computer, on the other hand, has no innate ability to recognize a face or a facial feature and must be programmed with an algorithm to do so. Generally, to recognize a face, different kinds of the facial features were used separately or in a combined manner. In the previous work, we have developed a machine learning based multi attribute face recognition algorithm and evaluated it different set of weights to each input attribute and performance wise it is low compared to proposed wavelet decomposition technique. Approach: In this study, wavelet decomposition technique has been applied as a preprocessing technique to enhance the input face images in order to reduce the loss of classification performance due to changes in facial appearance. The Experiment was specifically designed to investigate the gain in robustness against illumination and facial expression changes. Results: In this study, a wavelet based image decomposition technique has been proposed to enhance the performance by 8.54 percent of the previously designed system. Conclusion: The proposed model has been tested on face images with difference in expression and illumination condition with a dataset obtained from face image databases from Olivetti Research Laboratory.

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“…The extracted features from an image sequence can be described as a combination of three characteristic types: spectral (Toure and Beiji, 2010), spatial and temporal features. Spectral features relate to gray-scale or color information, spatial features associate with gradient or local structure and temporal features present inter-frame changes at the pixel.…”

Section: Introductionmentioning

confidence: 99%

A Block-Based Multi-Scale Background Extraction Algorithm

Davarpanah¹

2010

Journal of Computer Science

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Problem statement: To extract the moving objects, vision-based surveillance systems subtract the current image from a predefined background image. The efficiency of these systems mainly depends on accuracy of the extracted background image. It should be able to adapt to the changes continuously. In addition, especially in real-time applications the time complexity of this adaptation is a critical matter. Approach: In this study, to extract an adaptive background, a combination of blocking and multi-scale methods is presented. Because of being less sensitive to local movements, block-based techniques are proper to control the non-stationary objects' movements, especially in outdoor applications. They can be useful to reduce the effect of these objects on the extracted background. We also used the blocking method to intelligently select the regions which the temporal filtering has to be applied on. In addition, an amended multi-scale algorithm is introduced. This algorithm is a hybrid algorithm, a combination of some nonparametric and parametric filters. It uses a nonparametric filter in the spatial domain to initiate two primary backgrounds. In continue two adapted two-dimensional filters will be used to extract the final background. Results: The qualitative and quantitative results of our experiments certify not only the quality of the final extracted background is acceptable, but also its time consumption is approximately half in compare to the similar methods. Conclusion: Using Multi scaling filtering and applying the filters just to some selected nonoverlapped blocks reduce the time consumption of the extracting background algorithm.

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Intelligent Sensor for Image Control Point of Eigenfaces for Face Recognition

Cited by 7 publications

References 10 publications

Eigenface Algorithm-Based Facial Expression Recognition in Conversations - An Experimental Study

Eigenface Algorithm-Based Facial Expression Recognition in Conversations - An Experimental Study

Improving the Performance of Machine Learning Based Multi Attribute Face Recognition Algorithm Using Wavelet Based Image Decomposition Technique

A Block-Based Multi-Scale Background Extraction Algorithm

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