Improved Haar Cascade Feature Extraction and Access Control Framework for Rich Internet Applications

Abstract

In the digital era, internet application access protection against malicious access is a major concern. The security aspects such as password, pattern lock, and biometric are commonly preferred in mobile applications. To enforce the security feature in all aspects the face recognition padlock framework is used to prevent unauthorized or malicious accesses. The proposed system will secure the rich internet applications from malicious accesses and threats. The improved haar cascade classifier effectively detects and recognizes the objects. The model will take a 4*4 feature matrix to train the positive and negative images. The positive image is the factual confident image whereas the negative image is everything else other than the confident image. The system will take 100 good samples to train the model. The internet application is eligible to access only when the confidence rate is greater than 90 percent. The confident rate is the match probability of the trained image and actual image.