Human race detection using face with deep- learning technique is active research area. It helps expand growing areas like Human-Computer-Interface, Understanding user demographics. It provides great insight in better understanding of demographics and diversity among population. Understanding ethnic diversification among user base can help many commercial applications to improve and optimize their products and services better suited for the community needs. Development around race detection is already an active area of research and improving its performance with speed is one of them. In this study we compared FaceNet architecture-based features extraction technique to detect the race and compared with plain CNN based classification techniques. Comparative results support the claim that race detection problem is better handled by such embedding based approach than plain image classification approach. Embedding based techniques also provide competitive edge over other methods used in this comparative study.