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This article consists in implementing a Cloud-based service that provides the most advanced face recognition and detection algorithms with attributes under the Microsoft Azure platform. For its implementation, in the state of the art, biometric techniques used today for the recognition of facial patterns are analyzed and a general approach is to consider the existence of noise in the images to be analyzed when comparing them with the databases considering the alignment, normalization and scaling of each of the images tested with this service. With regard to the implementation of these services, differentiated experiments have been carried out in each of the phases of the project development, so that their strengths and weaknesses in the Cloud service can be evaluated. The analysis of the processed images has focused on observing the accuracy potential, efficiency and speed of the service in the Cloud. In addition, it was necessary to carry out an anthropometric study as an experimental basis to test the service and thus carry out a more thorough analysis of the face, considering in the project the following main functions: the detection of faces with attributes and facial recognition. The development of the project has two main lines of work: in the first line a service was implemented based on the libraries of the Face API of Microsoft Azure for facial recognition in C # whose performance was evaluated with a local database and later in the Cloud from Microsoft, the design and implementation was later adapted and improved for its real-time operation, the second line of work has an experimental approach, carrying out differentiated tests of the service in each of the development stages, where it was possible to carry out an evaluation in detail. The experiments focused on the study of the most relevant stages for the analysis of accuracy, performance and speed in the functions of: grouping, detection, checking, identification and comparison of faces and recognition of emotions.

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