This is a fast and optimized algorithm for hand geometry recognition based on Neural Networks. Proposed approach does not require any particular hardware since extracted features are computed without assuming any fixed hand positioning and also a low-cost webcam can be used for image acquisition. Code has been tested on CASIA Multi-Spectral Palm print Image Database.
Signatures are a special case of handwriting in which special characters and flourishes are viable. Signature Verification is a difficult pattern recognition problem as because no two genuine signatures of a person are precisely the same. There are two approaches to signature verification, online and offline. In offline case signature is obtained on a piece of paper and later scanned. While in online case signature is obtained on an electronic tablet and pen.
The code consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was normalized and the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise template. The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed.
Ear Recognition is a based on Principle Component Analysis for identification using the shape of the ear. The ear recognition techniques has become a key issue in ear identification and analysis for many geometric applications. This code reviews the source of ear image identification, compares the different applied models being currently used for the ear imagemodeling, details the algorithms, methods and processing steps and finally tracks the error and limitation from the input database for the final result obtain for ear identification.
Fingerprint identification is a widely used biometric identification system. This code is based on matching fingerprints using Euclidean Distance and Gabor Filters. The algorithm used in this code uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length Finger Code. Identification is based on the Euclidean distance between the two corresponding Finger Codes and hence is extremely fast and accurate than the minutiae based one. Accuracy of the system is 98.22%.
This program takes snapshot of car license number plate and then recognize the text on it. It is based on the very elementary technique of templates matching. The algorithm takes an input image of the number plate (number plate should be dominant in the image) and after filtering the image, it performs region based operations. Then it tries to capture the characters regions in a processed binary image and with the aid of template matching outputs the string of number plate characters.
In this GUI, almost all the basic and advance functions of image processing are combined to produce a nice effect. This code is a master piece. The study of this code will enable the user to master of making GUI in Matlab. This is the most selling item of our website since 2013.
This program calculate the similarity index of two images and tells user in terms of percentage that how much the two images are similar and where the difference lies. The images of the person should be of same posture. It is best for learning how to compare two images that are quite similar to each other.
This program performs Arabic and Urdu language optical character recognition. The program displays a graphical user interface where a user can input text embedded in image. The program performs a series of mathematical operations and then display the results in the form of simple characters which can be combine to make words.
Steganography is a method of hiding digital information. It allows for large quantities of information to be hidden inside a file, while making no perceivable changes to that file's contents. Steganography can be applied to many types of data, including audio, video, and images and can hide any kind of digital information.
This program displays a video player built in Matlab. The video player has the following capabilities. (1) Browse any video from any folder. (2) Start playing video (3) Pause Video and resume it (4) Exit video player (5)This code is a good start to learn video processing in Matlab.
This program extracts blood vessels from a retina image using Kirsch's Templates. Filtering of the input retina image is done with the Kirsch's Templates in different orientations. The threshold used in the program, can be varied to fine tune the output blood vessel extracted image.
This code brings real time face recognition sing Eigen face technique. So far people have used Eigen face technique only with stored data. But this project offers both real time capture and face recognition along with database faces so that you can validate the accuracy of the system.
This code finds the diameter/radius of large circular objects by using image processing techniques. You need to calibrate it first to get desired results.
This code finds the difference between two colors by using deltaE.
This code finds the difference between two currency notes
Smart parking ticket generation system.
In this program we implemented the face recognition algorithm via Sparse Representation. A sparse approximation is a sparse vector that approximately solves a system of equations.
Voice recognition is a process by which an algorithm authenticates the claimed identity of a person from his/her voice characteristics. A major application area of such systems would be providing security for telephone mediated transaction systems where some form of anatomical or "biometric" identification is desirable. Due to the great potential shown by artificial neural networks in the ﬁeld of speech recognition, we can perform speaker verification. To prove the concept, the technique is applied to the classification of 2 speakers using a single utterence. A clustering algorithm partitions the input and output synaptic weights of the trained networks according to a Euclidean distance measure and it is found that the input synaptic weights appear to effectively characterize the speaker. The results demonstrate that the chosen ANN model can be used for speaker identiﬁcation and verification purposes.
Gait analysis is the systematic study of human motion. Gait analysis is used to assess and treat individuals with conditions affecting their ability to walk. It is also commonly used in sports biomechanics to help athletes to identify posture-related problems. Recently, there has been much interest in machine vision systems that can duplicate and improve human gate recognition ability for biometric identification. While gait has several attractive properties as a biometric, there are several confounding factors such as variations due to footwear, terrain, fatigue, injury, and passage of time. This Project gives an overview of the factors that affect both human and machine recognition of gaits, data used in gait and motion analysis, evaluation methods, existing gait and quasi gait recognition systems, and uses of gait analysis beyond biometric identification. We compare the reported recognition rates as a function of sample size for several published gait recognition systems.