Face Detection Methods: A Comparative Study Between Viola-Jones and Skin Color Detection

Hussein, Maryam Mahmood; Mutlag, Ammar Hussein

doi:10.36478/jeasci.2019.4754.4760

Cited by 4 publications

(3 citation statements)

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“…The architecture of the proposed model for emotional state detection is presented in Figure 2. To extract emotional features from the facial images, the face region needs to be separated from the input image, for which the well-known Viola-Jones algorithm [21] is used. Additionally, to make the speech signals compatible with the CNN model, the Short-Time Fourier Transform (STFT) technique [22] is employed, which allows the description of each speech signal in a matrix form based on time and frequency.…”

Section: A Extraction Of User Emotional States Based On Deep Learningmentioning

confidence: 99%

“…Otherwise, the facial region in the input image will be determined. The computational details of this algorithm are described in [21], and we will not delve into them here. Finally, the facial region is cropped from the image and transformed into a 100 × 100-pixel matrix.…”

Section: A1 Preprocessing Of Facial Imagesmentioning

confidence: 99%

“…Figure 2: The architecture of proposed deep learning model for emotion recognition based on audio-visual features According to Figure 2, each speech signal and captured facial image are processed by separate CNN models. The task of these CNN models is to extract features related to emotions from the facial images and speech signals.To extract emotional features from the facial images, the face region needs to be separated from the input image, for which the well-known Viola-Jones algorithm[21] is used. Additionally, to make the speech signals compatible with the CNN model, the Short-Time Fourier Transform (STFT) technique[22] is employed, which allows the description of each speech signal in a matrix form based on time and frequency.…”

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Application of Image Classification Based on Improved LSTM in Internet Reading Therapy Platform

Xiong,

Yin,

Pan

2024

IEEE Access

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Reading therapy is an effective approach for improving mental states or addressing disabilities associated with individuals' dyslexia. In traditional approaches, this process is performed through the intervention and supervision of an expert, which incurs time and cost. However, by utilizing artificial intelligence technologies, the reading therapy process can be automated. This article focuses on presenting an internet-based automated platform for reading therapy. In this method, audio and visual features during the reading therapy are processed using deep learning techniques to identify the individual's emotional state based on their reading status. In this state, two separate convolutional neural networks are used to describe the facial image features and speech characteristics of the individual. Then, the described features from these two models are merged to determine the individual's mental states using LSTM layers. Finally, a reinforcement learning model is used to provide feedback and design subsequent exercises. This reinforcement approach leads to continuous improvement of the evaluated process and plays a significant role in enhancing the efficiency of the internet-based reading therapy system. The performance of the proposed method has been evaluated based on information from 20 volunteers. According to the results, the proposed method can effectively improve individuals' mental states and compete with the conventional supervisor-based approaches. The performance of the proposed deep learning models in identifying emotional states has also been investigated. The results indicate that this model achieves a minimum improvement of 9.71% in emotional state recognition compared to previous research, with an average correlation coefficient of 0.64485. INDEX TERMSInternet-based reading therapy, reading therapy framework, deep learning, fuzzy logic, reinforcement learning I.

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Section: A Extraction Of User Emotional States Based On Deep Learningmentioning

confidence: 99%

Section: A1 Preprocessing Of Facial Imagesmentioning

confidence: 99%

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