Human key point tracking such as face detection and recognition has become an increasingly popular research topic. It is a platform independent functionality and already being implemented on a wide range of platforms. Android is one such platform that runs on mobile phones and top of many edge devices such as car devices and smart home appliances. In the current times, AI and ML related applications are slightly moving into those edge devices due to various reasons such as security and low latency. The hardware enhancements are also backing this trend that happened over the last few years. Many solutions and algorithms have been proposed in this context, and various frameworks and models have also been developed. Even though there are different models available, they tend to deliver varying results in terms of performance. Evaluating these different alternatives to find an optimized solution is a problem worth addressing. In this thesis project, several selected face detection and recognition models have been implemented in an Android device, and their performance been evaluated. Google ML Kit showed the best results among the face detection methods since it took only around 68 milliseconds on average to detect a face. Out of the three face recognition algorithms evaluated, FaceNet was the most accurate as it showed an accuracy above 95% for most cases. Meanwhile, MobileFaceNet was the fastest algorithm, and it took only around 90 milliseconds on average to produce and output. Eventually, a face recognition application was also developed using the best performing models selected from the experiment.