Abstract
This research paper focus on a Machine learning model named Face Lock Algorithm with Gender and Age Classifier which will detect the face of the user using a face classifier called Haar Cascade Frontal Face classifier and will also provide an extra layer of security to mobile applications and websites by unlocking them only when the algorithm confirms the person as actual user. For this, it will first take the training data as an input from the camera of the device, then train the model based on the input and detect the face according to the training. Training will be done using LBPH (Local Binary Pattern Histogram) model which uses the concept of sliding window and applies the LBP operation on the image, which includes calculating pixel values of the image, finding threshold and then convert the image into binary. By doing so, all the important features of the image are extracted. Then it makes histograms for face recognition. In addition to this, after detecting the person's face, this algorithm will also classify the gender and gives a prediction about the age of the person. The primary advantage of this model is it makes use of a library called OpenCV for image capturing, which makes the implementation very simple as compared to the one's that use neural networks. Moreover, this algorithm can be used for a variety of purposes such as face lock gender and age classifier.