Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization
Algorithm 1
CADLVQ.
Input: The SURF extracted feature vectors (FV).
For M Users, Y view face images and N Parameter Sets (PS):
Initialization: Initialization of PS by pretraining the DNN layer by layer with different PS, setting the weights, W, to zero; using the K-means algorithm to quantise the FV vectors
Output: The confusion matrix including Sensitivity, Specificity, Precision, Accuracy, and F1score of the matching scores produced for each class with different parameter sets (PS). A decision (Accept/Reject) based on confidence level.
Procedure:
(1)
Setting the FV of a single-layer RBM network.
(2)
FV updating based on the gradient, weight decay, and momentum.
(3)
Inference determination based on posterior probability over the hidden variable.
(4)
Determination of expectation maximization (EM) to provide sufficient information for unobserved data.
(5)
Apply the softmax of PS activation parameter vectors:
(6)
Generate the codewords by using the K-means algorithm.
(7)
Classification of Bag of Words (BoW) Using K-NN
(8)
Determination of the similarity metric between the query and stored templates using Euclidean distance with dynamic metric adaptation.
