Effect of Successive Convolution Layers to Detect Gender - Ahmed, Hanaa Mohsin; Mahmoud, Halah Hasan (Iraqi Journal of Science, 2018)
Image classification can be defined as one of the most important tasks in the area of machine learning. Recently, deep neural networks, especially deep convolution networks, have participated greatly in end-to-end learning which reduce need for human designed features in the image recognition like Convolution Neural Network. It is offers the computation models which are made up of several processing layers for learning data representations with several abstraction levels. In this work, a pre-trained deep CNN is utilized according to some parameters like filter size, no of convolution, pooling, fully connected and type of activation function which includes 300 images for training and predict 100 image gender using probability measures. Results in Classification and precision accuracy equal to 0.68 and 0.3225 respectively.