Modified DSFD and TCDCN Based Facial Landmark Detection for Gender and Age Classification

Facial analysis evaluates the physical appearances of person, which is crucial for several clinical settings. The perspective of real-world faces captured in an uncontrolled environment makes it harder for the gender prediction algorithm to correctly identify gender. The accuracy of the most advanced algorithms currently in use for real-time facial gender prediction is decreased by these factors. Most importantly the facial gender prediction can pave a way for the visually challenged persons to identify the gender and age. A dual shot face detector with task restricted Fine-tuned deep neural network (DTFN) is created to recognise the facial land markings for accurate gender and age prediction in order to overcome the challenges and defects. Bidirectional filtering and sigmoid stretching are the main preprocessing methods used to improve contrast and remove noise from the input image once facial photographs are first gathered. Next, employing the modified dual shot face detector (DSFD) to separate the face from the remaining background image. To solve this problem, DSFD is built around caps net. A task constrained deep convolutional neural network (TCDCN) is then used to extract and identify features from facial landmarks. The collected features are fed into a fine tuned deep neural network (DNN) classifier, which further classifies the data according to age and gender. By adjusting the hidden layer’s parameter using the stochastic gradient descent technique, fine tweaking is achieved. According to the results of experimental research the proposed technique achieves 96 accuracy, precision has 96.57, and hit rate value of 96. Thus, the proposed approach is the best option for automatic facial land mark detection.