Papers by Dasun Perera
Thesis Chapters by Dasun Perera
Face recognition technology has been one of the most important fields that emerged during past tw... more Face recognition technology has been one of the most important fields that emerged during past two decades since the demand for identifying a person by analysing an image escalated exponentially.
A face recognition system is a computer application which identifies and verify a person’s face automatically from a digital image. To successfully identify a face, a given face’s facial features would be compared to already existing face database’s facial features. The most similar image would be selected and presented as the identified face for the given face. Facial recognition could be used in many applications of security stream such as passport photo verification, access control, payment verifications, criminal identification and many more. In this project, a generic face recognition application is developed which could be adopted in many streams.
There are there main phases in a face recognition system. First phase is acquiring images and pre-possessing them. Pre-processing images would help to reduce the drastic changes of images with the illumination of each input image. Furthermore it would help to process the images easily by reducing dimensions and would increase the accuracy of identifying a face and decrease the processing time. The second Phase is training the data set. It is important to have a database of faces of each individual which we can use to compare with the input face. The last phase is identification of a given face.
Principal Component Analysis (PCA) is a commonly used feature extraction technique and in this project I have illustrated how it is implemented to reduce the dimensions and how it could work with Euclidian distance image classifier to identify a person’s image successfully.
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Papers by Dasun Perera
Thesis Chapters by Dasun Perera
A face recognition system is a computer application which identifies and verify a person’s face automatically from a digital image. To successfully identify a face, a given face’s facial features would be compared to already existing face database’s facial features. The most similar image would be selected and presented as the identified face for the given face. Facial recognition could be used in many applications of security stream such as passport photo verification, access control, payment verifications, criminal identification and many more. In this project, a generic face recognition application is developed which could be adopted in many streams.
There are there main phases in a face recognition system. First phase is acquiring images and pre-possessing them. Pre-processing images would help to reduce the drastic changes of images with the illumination of each input image. Furthermore it would help to process the images easily by reducing dimensions and would increase the accuracy of identifying a face and decrease the processing time. The second Phase is training the data set. It is important to have a database of faces of each individual which we can use to compare with the input face. The last phase is identification of a given face.
Principal Component Analysis (PCA) is a commonly used feature extraction technique and in this project I have illustrated how it is implemented to reduce the dimensions and how it could work with Euclidian distance image classifier to identify a person’s image successfully.