7.2. Face recognition
In order to verify the effectiveness
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 . 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
 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.