All the convolutional layers used are capable of detection patterns. Within each convolutional layer, we used 4 filters. The input image to first-part CNN (used for background removal) generally consists of shapes, edges, textures, and objects along with the face. The edge detector, circle detector, and corner detector filters are used here at the start of the convolutional layer 1 of this first CNN. Once the face is detected, in the second layer CNN filters detect eyes, ears, lips, nose, and cheeks. The edge detection filters in the layer is as shown in figure 3 (a). The second-part CNN consists of layers with 3x3 kernel matrix e.g. [0.25, 0.17, 0.9; 0.89, 0.36, 0.63; 0.7, 0.24, 0.82]. These numbers are selected between 0 to 1 initially and later optimized for EV detection based on ground-truth in the supervisory training dataset. Here we used minimum error decoding to optimize filter values. once the filter is tuned it is applied to background removed face, for detection of different facial parts (e.g. eye, lips. nose, ears etc.) To generate EV matrix we take 24 different features. This EV feature vector is nothing but values of normalized euclidian distance between each of face part as shown in figure 3 (b).