Hand Gesture based Person Identification has become a promising technology with a wide range of applications for Human Computer Interaction. Hand gestures are recognized by Kinect sensors, Gyro sensors and Inertial Sensors. We have used Frequency Modulated Continuous Wave Radar based hand gestures dataset of 6 people with four different gestures for identifying each Person. Previous works have identified hand gestures with Radar as its sensors, in this work we have proposed a dilated Convolution Neural Network Model for identifying the Person based on micro-Doppler hand gestures using a reduced number of parameters in contrast to existing state of art methods, that work with larger number of parameters for learning. We have observed that the proposed model has achieved an excellent recognition rate of 97.47% with the test data and 68% reduced parameters compared to benchmark and traditional CNN with minimal memory utilization and optimized number of computations of the Proposed Model.