System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Abstract

Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system.

The Effects of Facial Expressions on Face Biometric System’s Reliability

“…After obtaining results of the RSS and SS between neutral mode and each expression for 36 subjects, the results showed that the following FEs; happy, sadness, surprise, anger, disgust, and fear showed a lower SS compared to neutral mode. The highest dissimilar FE was achieved by the disgust expression of 92.01%, which was also agreed with results of Azimi [36] and Márquez-Olivera [37]. While the lowest one achieved by sad expression 93.94%, which is contrary to what has been reported in [37] where it was the happy expression.…”

“…The highest dissimilar FE was achieved by the disgust expression of 92.01%, which was also agreed with results of Azimi [36] and Márquez-Olivera [37]. While the lowest one achieved by sad expression 93.94%, which is contrary to what has been reported in [37] where it was the happy expression. The following expressions: fear, surprise, anger, happy achieved 93.34%, 92.66%, 92.50%, 92.40% respectively, as shown in Table 10 and Figure 21.…”

“…His results showed the following: (1) by comparing the neutral faces and FE, the average genuine similarity has been degraded; (2) sadness and disgust expressions are the most dissimilar expressions among the other expressions; (3) the best class to be verified with was the normal face, as there was no facial deformation and muscle movement; (4) For users who enrolled with happy, angry, surprised, disgusted, sad, and fearful expressions, thebest expressions to verify with are fearful, sad, fearful, sad, fearful expressions, respectively; (5) the lowest matching score was achieved when users who provided happy, angry, surprised, disgusted, sad, fearful faces during the enrollment, identified themselves with disgusted, surprised, angry, happy, angry faces respectively during the verification. Another study investigated the effect of FE on FB systems by Márquez-Olivera et al [37], where they analyzed their FB system under the influence of FE. It has been concluded that failures occurred when the subjects expressed surprise as it has maximum facial deformations while the sadness and anger expressions express high deformation on eye regions.…”

“…(b) Happy Expression. (c) Sadness Expression (d) Surprise Expression (e) Anger Expression (f) Disgust Expression (g) Fear Expression Subject 1 in IMPA-FACES3D[37] shows the following expressions: (a) neutral, (b) happy, (c) sadness, (d) surprise, (e) anger, (f) disgust, (g) fear[40].…”

“…Subject 1 in IMPA-FACES3D[37] shows the following expressions: (a) neutral, (b) happy, (c) sadness, (d) surprise, (e) anger, (f) disgust, (g) fear[40].…”