Khalid Ounachad et al.,International Journal of Emerging Trends in Engineering Research, 8(7), July 2020, 3538 – 3545 3538 ABSTRACT Machine learning is a subarea of artificial intelligence based on the idea that systems can learn from data and make decisions automatically. Bayes Theorem is widely used in machine learning. The main objective of this paper is to classify the gender of the human being based on their face sketch images by using a golden ratio features and Bayes Classifier. This paper presents a method for human face sketch gender classification and recognition. It is inspired in our other model which was pre-trained on the same task, but with sixteen features and fuzzy approach. Toward this end, just two features will be extract from the input face sketch image based on two face golden ratios. The detection stage passes by Viola and Jones algorithm. The classification task is evaluated through Bayes classifier. An experimental evaluation demonstrates the satisfactory performance of our approach on CUFS database with 80% for training, 20% for testing. The proposed machine learning algorithm will be a competitor of the proposed relative the stat of the art approaches. Key words : CUFS Database, Facial gender recognition, Forensic sketches, Gender classification, Golden Ratio, Machine learning. 1. INTRODUCTION Face Gender Recognition (FGR) system is a major area for non-verbal language in day to day life communication. FGR systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problem of images classification, also due to its large-scale applications in face analysis, particularly face recognition [1]. Gender based separation among humans is classified into two: male and female [2]. Face Gender Classification (FGC) systems aim to automatically classify gender in a dataset of photos or sketches images (Figure.1). It based on two-dimensional images of human subjects. Currently gender classification and recognition from facial imagery has grown its importance in the computer vision field: It play a very important function in many fields likes, face recognition [1][3], forensic crime detection [4][5], facial emotion recognition[3] and psychologically affected patients [6], night surveillance [7] and Artificial Intelligence[8][9] and soon. In this paper it can be used to identify; fastly; a criminal person from his sketch for purposes of identification [10]. Humans have a natural behaviour and ability to extract, analyze, identify, and interpret informations encrypted in the face features likes gender. The automatic task of facial gender recognition is a challenging work and explicitly difficult: Human gender classification and recognition can be done in many ways. In this paper is concerned with the gender classification based on two-dimensional images of people's face sketches. There is a large number of databases available for human sketches gender classification and recognition research, some of them are private and some are public. The CUFS [11] is most commonly used in face sketch recognition scenario[5]. Figure 1:In the left, an input image sketch to the Facial sketch gender classification system. In the right, the output result. It indicates the detected gender with accuracy. (image is a Portrait purportedly of Bayes Thomas Bayes who is known for formulating Bayes' theorem. (images ©Victory Graphik)) Golden Ratio and Its Application to Bayes Classifier Based Face Sketch Gender Classification and Recognition Khalid Ounachad 1 , Mohamed Oualla 2 , Abdelalim Sadiq 3 1,3 Department of Informatics, Faculty of sciences, Ibn Tofail University, Kenitra, Morocco khalid.ounachad@uit.ac.ma a.sadiq@uit.ac.ma 2 SEISE: Software Engineering & Information Systems Engineering Team, Faculty of sciences & technology, Moulay Ismail University, Errachidia, Morocco, mohamedoualla76@gmail.com ISSN 2347 - 3983 Volume 8. No. 7, July 2020 International Journal of Emerging Trends in Engineering Research Available Online at http://www.warse.org/IJETER/static/pdf/file/ijeter107872020.pdf https://doi.org/10.30534/ijeter/2020/107872020