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In the case of the HOG feature descriptor, for example, the input image is of size 112 x 92 x 3, and the output feature vector is of length 4680.
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Typically, a feature descriptor converts an image of size “width x height x 3” (channels) to a feature vector/array of length n.
#Human activity detection matlab code Patch#
A feature descriptor is the representation of an image or an image patch that simplifies the image by extracting useful information and throwing away extraneous information. The technique counts occurrences of gradient orientation in localized portions of an image. HOG is a feature descriptor used in computer vision and image processing for the purpose of object detection. The method used in this project is the Histogram of Oriented Gradients (HOG) feature descriptor. This method also used in feature extraction for face recognition.
![human activity detection matlab code human activity detection matlab code](http://www.heathcaldwell.com/yahoo_site_admin/assets/images/Illustrious_1.10724228_std.jpg)
In general appearance-based methods rely on techniques from statistical analysis and machine learning to find the relevant characteristics of face images. The appearance-based approach is better than other ways of performance. The appearance-based method depends on a set of delegate training face images to find out face models. However, deformable templates have been proposed to deal with these problems. This approach is simple to implement, but it is inadequate for face detection. Also, a face model can be built by edges just by using edge detection methods. Ex- a human face can be divided into eyes, face contour, nose, and mouth. Template Matching method uses pre-defined or parameterized face templates to locate or detect the faces by the correlation between the templates and input images. This approach divided into several steps and even photos with many faces they report a success rate of 94%. The idea is to overcome the limits of our instinctive knowledge of faces. It is first trained as a classifier and then used to differentiate between facial and non-facial regions. The feature-based method is to locate faces by extracting structural features of the face. This approach alone is insufficient and unable to find many faces in multiple images. There could be many false positives if the rules were too general or too detailed. The big problem with these methods is the difficulty in building an appropriate set of rules. Ex- A face must have a nose, eyes, and mouth within certain distances and positions with each other. The knowledge-based method depends on the set of rules, and it is based on human knowledge to detect the faces. In general, these methods divided into four categories, and the face detection algorithms could belong to two or more groups.
![human activity detection matlab code human activity detection matlab code](https://www.mathworks.com/help/audio/ref/description064a1ec43014dbc38949c5e9d7f3a98a.png)
We can use computer vision techniques to perform feature extraction to encode the discriminative information required for face recognition as a compact feature vector using techniques and algorithms such as:
#Human activity detection matlab code code#
The written code has been attached in appendix 1 at the end of this project. This method has the advantage of recognizing the faces basing on the different facial gestures. The results showed a very good ability of HOG method to recognize faces with an accuracy of about 90%. The implementation of this method has been done using the MATLAB environment. In this project, we introduce an application of the HOG feature method for face recognition. Face recognition leverages computer vision to extract discriminative information from facial images, and pattern recognition or machine learning techniques to model the appearance of faces and to classify them. Face recognition is an important part of many biometric, security, and surveillance systems, as well as image and video indexing systems. Algorithms for face recognition typically extract facial features and compare them to a database to find the best match. Face recognition is the process of identifying one or more people in images or videos by analyzing and comparing patterns.