**Nuria Gómez Blas 1,†,‡, Luis Fernando de Mingo López 1,\*,†,‡, Alberto Arteta Albert 2,† and Javier Martínez Llamas 1,†,‡**


Received: 19 March 2020; Accepted: 27 April 2020; Published: 29 April 2020

**Abstract:** While whale cataloging provides the opportunity to demonstrate the potential of bio preservation as sustainable development, it is essential to have automatic identification models. This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens by processing their tails patterns. This work collects datasets of composed images of whale tails, then trains a neural network by analyzing and pre-processing images with TensorFlow and Keras frameworks. This paper focuses on an identification problem, that is, since it is an identification challenge, each whale is a separate class and whales were photographed multiple times and one attempts to identify a whale class in the testing set. Other possible alternatives with lower cost are also introduced and are the subject of discussion in this paper. This paper reports about a network that is not necessarily the best one in terms of accuracy, but this work tries to minimize resources using an image downsampling and a small architecture, interesting for embedded system.

**Keywords:** convolutional neural networks; pattern recognition; machine learning
