DL aims to learn from the training

7.2. Face recognition
In order to verify the effectiveness image of MLDL for face recognition, we perform experiments on two widely used face image datasets, namely the Multi-PIE and AR datasets.
7.2.1. Experiment on the Multi-PIE dataset
Multi-PIE dataset contains more than 750,000 images of 337 people under various views, illuminations and expressions. More introductions about this BX517 dataset can be referred to the Literature [32]. Here, a subset of 68 peoples (24 samples for each people) with 5 different poses (C05, C07, C09, C27, and C29) is selected for experiment. The image size is 64×64 pixels. We replace a certain percentage of randomly selected pixels of each image with pixel value of 255. Fig. 2 exemplifies random pixel corruption on face images of one subject. Principal component analysis (PCA) transformation [56] is used to reduce the dimension of samples to 100. We randomly select 8 samples (each sample with 5 different poses) per class for training and use the remained samples for testing. And we repeat random selection 20 times and record average results.
Sign In or Register to comment.