A humanoid robot capable of playing soccer needs to know where opponents and teammates are in the soccer field. The robot has to be able to recognize teammates and opponents, inferring information such as distance and estimated location of the other robots. In order to achieve this key requisite, this paper analyze two descriptor algorithms, HAAR and HOG, so that one of them can be used for recognizing humanoid robots with less false positives alarms and with best frame per second rate. They were used with their respective classical classifiers, AdaBoost and SVM. As many different robots are available in RoboCup domain, the descriptor needs to describe features in a way that they can be distinguished from the background at the same time the classification has to have a good generalization capability. Although some limitations appeared in tests, the results were beyond expectations.