Recognizing people from their faces has a strong impact on social interaction. In this paper we present a pilot study on healthy people where brain activities during a face recognition task have been recorded using electroencephalogram (EEG). Target images (previously seen in a training phase), were presented in the recognition phase in two different conditions: identical to those of the initial phase, modified with biologically plausible changes (such as features enlargement or changed expression) and randomly presented with new faces. The raw EEG data were properly cleaned from both biological or non-physiological artifacts. Statistically significant differences in brain activations were registered between the two experimental conditions, especially in the frontal area, during the recognition process. The results of the analysis on this database of healthy people can be useful as baseline for further studies on people affected by congenital prosopagnosia or

/pdf/eeg-data-of-face-recognition-in-case-of-biological-ie6ya6leng.pdf

EEG data of face recognition in case of biological compatible changes: A pilot study on healthy people

Brain activity during perception and recognition of faces have been studied by researchers with the purpose to develop brain-computer interfaces and to study neurological disorders. In this paper, we analyzed evoked potentials as neurophysiological indicators and developed a model based on signal processing and machine learning techniques to find descriptive patterns that allow the differentiation of familiar and unfamiliar faces. We considered wave components such as P1, N170, N250, P300, and N400 to describe the events. Morphological analysis and wavelet transform were used for the feature extraction stage, and support vector machines and binomial logistic regression were evaluated for the classification stage. The best classification results were obtained with the morphological characteristics, where the highest classification accuracy was 80% on average.

Analysis and Classification of Evoked Potentials in Response to Familiar and Unfamiliar Faces

With the progress of science and technology and the rapid development of computer technology, the ability of computer to process information has been greatly improved. Biometric recognition technology based on digital image has gradually become an important exploration direction in the field of pattern recognition. Among the biometric recognition technologies, face has the characteristics of uniqueness, non-reproducibility, easy collection, and no need for the cooperation of researchers. It has better performance than other biometric recognition technologies. Therefore, face recognition has become one of the important research directions in the field of pattern recognition. This paper briefly introduces the background and current situation of feature extraction in face recognition, leads the analysis of the algorithm of face recognition system, and puts forward the method of exploring face feature extraction.

https://webofproceedings.org/proceedings_series/ESSP/ICEMC 2019/ICEMC19135.pdf

Research on Feature Extraction Method in Face Recognition

Recognizing people from their faces has a strong impact on social interaction.In this paper we present a pilot study on healthy people where brain activities during a face recognition task have been recorded using electroencephalogram (EEG).Target images (previously seen in a training phase), were presented in the recognition phase in two different conditions: identical to those of the initial phase, modified with biologically plausible changes (such as features enlargement or changed expression) and randomly presented with new faces.The raw EEG data were properly cleaned from both biological or non-physiological artifacts.Statistically significant differences in brain activations were registered between the two experimental conditions, especially in the frontal area, during the recognition process.The results of the analysis on this database of healthy people can be useful as baseline for further studies on people affected by congenital prosopagnosia or autism.

EEG Data of Face Recognition in Case of Biological Compatible Changes: A Pilot Study on Healthy People

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset condition characterized by difficulty paying attention and hyperactivity. The diagnosis of ADHD is made from psychological tests and electroencephalography (EEG). However, patient cooperation is necessary, which is a challenge with ADHD children. This work proposes a method for classification of ADHD and control cases from cognitive event-related potentials using recurrence plots and deep learning. A total of 44 children were included in this study (22 children with ADHD and 22 case controls). The signals were processed by a high-pass filter to eliminate DC components, wavelets transform with six decomposition levels, and synchronized averaging for each of the six channels (F3, AF3, F4, AF4, F7 and F8). Subsequently, the recurrence plot of each of the processed signals was obtained and used as inputs for two convolutional neural networks (CNN). The proposed models showed accuracies of 69.44% and 77,78%.

Classification of Cognitive Evoked Potentials for ADHD Detection in Children using Recurrence Plots and CNNs

Research on brain machine interface (BMI) has been developed very fast in recent years. Numerous feature extraction methods have successfully been applied to electroencephalogram (EEG) classification in various experiments. However, little effort has been spent on EEG based BMI systems regarding familiarity of human faces cognition. In this work, we have implemented and compared the classification performances of four common feature extraction methods, namely, common spatial pattern, principal component analysis, wavelet transform and interval features. High resolution EEG signals were collected from fifteen healthy subjects stimulated by equal number of familiar and novel faces. Principal component analysis outperforms other methods with average classification accuracy reaching 94.2% leading to possible real life applications. Our findings thereby may contribute to the BMI systems for face recognition.

Single trial EEG classification applied to a face recognition experiment using different feature extraction methods

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Single trial EEG classification applied to a face recognition experiment using different feature extraction methods