Improving Performance of Sparse Autoencoder by Using DPHOG for Gender Classification
Natthariya Laopracha, Rapeeporn Chamchong, Thawatwong Lawan · 2021
This paper proposes the improvement performance of Sparse Auto Encoder (SAE) by using Dominant Patterns of Histograms of Oriented Gradients (DPHOG). The SAE has a simple structure and fast computation. However, the SAE demonstrates less accuracy than the Convolution of Neuron Networks (CNN). This proposed method selects the dominant features of female and male face images, and then encoding and decoding these features within SAE. The researchers conducted DPHOG with SAE in two datasets and compared them with SAE, HOG with SAE, and CNN. Experimental results showed that DPHOG with SAE produced the highest performance in terms of accuracy, true positive rate, false positive rate and time computation in the two datasets. In addition, the DPHOG with SAE can extract learning features the form small dataset. In contrast, CNN requires a huge dataset for learning features.