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BRAIN: A Robust Approach of Facial Orientation Recognition from Facial Features

In the field of computer vision and pattern recognition, face orientation recognition stands as a significant topic. In the paper A Robust Approach of Facial Orientation Recognition from Facial Features, the authors Stefan Andrei, Kishor Datta Gupta, Md Manjurul Ahsan and Kazi Md. Rokibul Alam introduce us to an image mapping technique for face analysis.

The methodology of the study consists of two main phases: Face Feature Extraction and creating the graph image, and Matching Graph image with stored images. The Face Feature Extraction presents four steps that include Face Detection, Feature Extraction, Obtaining Feature Data and Creating image with these data.

The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The technique of this study relies on the four main features of the face: left eye, right eye, nose, and mouth. It is mandatory to obtain the positions, size, height, width, and angle of these features respective to each face. By acquiring the data from the features, a new picture including the model and shape of the face is created.

Experiment Results: Sample image data 1
Experiment Results: Sample image data 1

(more…)

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    In neurology and neuroscience research, Steady-State Visually Evoked Potential (SSVEP) are brain signals which occur in response to visual stimulation. The paper Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed - written by Abdullah Talha Sözer and Can Bülent Fidan - aims to introduce novel detection features for the SSVEP based brain computer interfaces. Brain-computer interface (BCI) is a collaboration between a brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. The interface enables direct communication between the brain and the object…
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  • 31
    The article written by Sarunya Kanjanawattana and Masaomi Kimura is a study about Optical Character Recognition (OCR), which represents a a typical tool used to transform image-based characters to computer editable characters. The two illustrate a novel method which is a combination of a graph componenet extraction and an OCR-error correction. In the last years, graphs became very important to researches, as they contain significant information which can be extracted and used. Graphs offer data summarization which presents essential information that is interpreted by acquiring small descriptive details. In order to succeed in obtaining a primary interpretation, OCR was created,…
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  • 29
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  • 24
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