ABSTRACT Facial recognition plays a significant role in different applications such as human computer communication, video surveillance, face tracking and face recognition. Efficient face recognition algorithm is required to accomplish such task. Face recognition is one of the most active research areas in computer vision and pattern recognition with practical applications. In present networked world, the need to conserve the security of information is becoming both increasingly significant and increasingly hard. In recent time, there has been an increasing spike in crimes related to identity theft and other forms fraudulent activities over the internet; examples are: credit card fraud, hacking, etc. The existing face recognition systems are able to circumvent the problem of identity theft but are not scalable and can’t run on low memory devices. In this project, a face recognition system that is scalable for mobile based identity sensitive applications like internet banking is developed. To achieve the scalability, the principal component analysis (PCA) is employed to reduce the dimensions of features of a face, which usually exist in high dimensions. Then, these features are classified using haar cascade classifier to achieve face recognition of a given person.
Mohammed, I (2021). Face Recognition On Principal Component Analysis. Afribary. Retrieved from https://tracking.afribary.com/works/face-recognition-on-principal-component-analysis
Mohammed, IBRAHIM "Face Recognition On Principal Component Analysis" Afribary. Afribary, 23 Apr. 2021, https://tracking.afribary.com/works/face-recognition-on-principal-component-analysis. Accessed 24 Nov. 2024.
Mohammed, IBRAHIM . "Face Recognition On Principal Component Analysis". Afribary, Afribary, 23 Apr. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/face-recognition-on-principal-component-analysis >.
Mohammed, IBRAHIM . "Face Recognition On Principal Component Analysis" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/face-recognition-on-principal-component-analysis