Convolutional Neural Network New Architecture lead to better Performance on Age/Gender estimate network

Without using a complex Age/Gender estimate system there is a probability that an autopilot taxi ask whereI have to go sir from a female traveler. CNN has a desired performance in many image processing jobs aswell as Age and Gender estimation Network, its accuracy has a limit which researches try to improve byoptimizing the network parameters, neuron type, lose functions, averaging result and so on. In our case wefound that the Standard CNN Definition itself is one of the limitation cap, The CNN Layer s Architectureconsists of a number of convolutional and subsampling layers optionally followed by fully connected layers,other CNN definition that are being used are same in architecture too, but replacing all the subsamplinglayers with another overall convolution layer did the job with the cost of double training time. We reached%2.5 & 4% more accuracy in Age & Gender estimate CNN.

Keywords:

Convolutional , Estimation , Age , Gender