Face recognition in video is an application of great interest in the scientific community and in the surveillance industry, boosting the search for efficient and robust techniques. Nowadays, in the facial recognition field, the frontal identification techniques are those with the best hit ratio when compared with others non-frontal techniques. This work has as main objective seek for methods to evaluate images in video to look for people (faces), assessing if the image quality is in an acceptable range that allows a facial recognition algorithm to identify the individuals. It s proposed ways to decrease the processing load to allow a maximum number of individuals assessed in an image without affecting the real time performance. This is reached through analysis of most the techniques used in the last years and the state-of-the-art, compiling all information to be applied in a project that uses the strengths of each one and offset its shortcomings. The outcome is a multithread platform. Performance evaluation was performed through computational load tests by using public videos available in AVSS ( Advanced Video and Signal based Surveillance). The outcomes show that the architecture makes a better use of the computational resources, allowing use of a wide range of algorithms in every segment of the architecture that can be selected according to quality image and video environment criteria.