Detect faces and features with precision using OpenCV in this GitHub repository. Implement robust algorithms for facial recognition, eye detection, and more, with comprehensive documentation and easy-to-follow examples for seamless integration into projects.
- Features are qualities of an object
- Salient attribute or components of an object within an image scence
- ideally invariant to transformations
- May be identified by classifiers
Detection is often a step prior ti recognition Example - The image at the right detects faces .A postdetection process might be to try and recognize a face matching a database of classifiers. in this case features such as distance between eyes in an image, may be use both for the detection process to see whether or not a face actually exists, but also used as a classifire for the recognition process to see which face is specifically matchs with.
in here there will two algorithms
- Template maching for general object recognition
- Haar cascading as mean for face detection
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Search a smimiler pattern between two images
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taking a referance image call tempate and sliding it around the other comparison image taking difference every position.
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result - black and whie gray scale image with varying intensities showing how well match each position.
- A form of features-based machine learning
- Uses pretrain images of labeled positives and negatives
- Runs through thousands of classifires in a cascaded manner
- Used case : Detect faces in the image and draw bounding boxes.
