数据库 Resources

Showing items tagged with "数据库"

This package contains research code accompanying a paper on non-ideal iris recognition systems, including systematic study tools and a small sample image collection (20 users). The implementation features batch processing capabilities for iris image analysis and supports angle-variant iris capture scenarios (0°, 15°, 30° frontal views). Researchers needing full access to West Virginia University's non-ideal iris database should contact the specified researchers for complete dataset acquisition.

MATLAB 299 views Tagged
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A MATLAB-implemented face recognition system based on Markov Model and Support Vector Machine with integrated face database. This robust implementation (non-original) demonstrates excellent performance with key features: database generation from training/test samples; face recognition rate calculation (96.5 ); specific image identification; real-time camera-based face recognition. The system utilizes probability transition matrices for feature extraction and SVM classification for pattern recognition.

MATLAB 313 views Tagged
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ECG signal detection workflow: downloading signals from databases, implementing filtering algorithms, and performing noise removal processes

MATLAB 287 views Tagged
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This database comprises 213 grayscale images representing 7 distinct positive facial expressions from 10 subjects. All images are stored as 256×256 pixel 8-bit grayscale TIFF files, with an average of 2-4 samples per expression per individual. The dataset structure facilitates implementation of facial expression recognition algorithms through standardized image preprocessing and classification techniques.

MATLAB 265 views Tagged
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The system consists of the following components: capturing images using a computer's built-in camera, face detection, storing detected face images in a database, and performing face recognition using input photos. This program implements face detection through a skin color recognition approach, where the facial skin color range is defined as 100≤B≤120 and 140≤R≤160. Pixels within this range are set to white while the remaining pixels are set to black. The algorithm employs the imerode function for spherical erosion and applies median filtering to achieve smoothing effects. Finally, after scaling, binarization, and various processing stages, regions with fewer than 1000 white pixels are discarded. Image segmentation incorporates Euler numbers to eliminate background areas resembling facial colors.

MATLAB 302 views Tagged
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Sparse representation-based face recognition using the ORL database, containing 40 subjects with 10 images each. The implementation randomly selects training samples while using the remaining images as test samples. The final recognition accuracy is calculated as the average of 20 independent trials to ensure statistical reliability.

MATLAB 267 views Tagged
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