Authors:
Mahsa Kiazadeh
1
;
Gabriela Goncalves
2
and
Hamid Reza Shahbazkia
3
Affiliations:
1
DEEI, FCT Universidade de Algarve, Faro, Portugal
;
2
Departamento de Psicologia e Ciencias de Educação, Universidade do Algarve, Faro, Portugal
;
3
SAS, University of Central Asia Naryn Campus, Naryn, Kyrgyzstan
Keyword(s):
Facial Landmarks, Human Ovulation, Fluctuant Asymmetry, Facial Features, Attractiveness.
Abstract:
This document tries to study the truth about human concealed ovulation only by analysing possible facial modifications. In normal view, the human ovulation remains concealed. In other words, there is no visible external sign of the mensal period in humans. These external signs are very much visible in many animals such as baboons, dogs or elephants. Some are visual (baboons) and others are biochemical (dogs). Insects use pheromones and other animals can use sounds to inform the partners of their fertility period. The objective is not just to study the visual female ovulation signs but also to understand and explain automatic image processing methods which could be used to extract precise landmarks from the facial pictures. This could later be applied to the studies of fluctuant asymmetry. The field of fluctuant asymmetry is a growing field in evolutionary biology but cannot be easily developed because of the time necessary to extract landmarks manually. In this work we have tried to
see if such signs are present in human face during the ovulation could be detected, either by computer vision or by human observers. We have taken photography from 50 girls for 32 days. Each day we took many photos. At the end we chose a set of 600 photos, 15 photos per girl representing the whole mensal cycle of 40 women. The photos were organized in a rating software to allow human raters to watch and choose the 2 best looking pictures for each girl. These results were then checked to highlight the relation between chosen photos and ovulation period in the cycle. The results, were indicating that in fact there are some clues in the face of human which could eventually give a hint about their ovulation. Later, different automatic landmark detection methods were applied to the pictures to detect landmarks which could show the changes in the face during the period. Although the precision of methods tested are far from being perfect, but the comparison of these measurements to the state of art indexes of beauty shows a slight modification of the face towards a prettier face during the ovulation. The automatic methods tested were Active Appearance Model (AAM), the neural deep learning and the regression trees. It was observed that for this kind of applications the best method was the regression trees. Future work has to be conducted to firmly confirm these data, number of human raters should be augmented and a proper learning data base should be developed to allow a learning process specific to this problematic. We also think that low level image processing will be necessary to achieve the final precision which could reveal more details of possible changes in human faces.
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