Facial Expression Detection Using Neural Network for Customer Based Service

Ramani, Sandeep Kumar

doi:10.1109/icccsp.2018.8452864

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…From Table 2, it can be concluded that the happy expression has the highest success rate which is 89% and the neutral expression which success rate is 84% and the lowest success rate is the sad expression which is 75%. [26]. In our proposed model, a fully multi-connect layer is applied in the training process with softmax function which helps the model to obtain its highest accuracy result from the given input and provide a success rate of 89 % as happy, 75% as sad, and 84% as neutral.…”

Section: Results and Analysismentioning

confidence: 99%

“…Sandeep developed a model to detect customer facial expression by using neural network [26] on a VGG-16 architecture. The VGG-16 architecture [27] can process large-scale image recognition and extract features.…”

Section: Introductionmentioning

confidence: 99%

“…The VGG-16 architecture [27] can process large-scale image recognition and extract features. S. K. Ramani [26], the CNN network is considered as the baseline of the system and it uses the Google cloud platform vision application programming interface (API) for facial expression detection which could also help to minimize the computational time for processing the images. To capture the corresponding expression a camera is attached with Rasberry Pi which records the customer's facial expression every five seconds.…”

Section: Introductionmentioning

confidence: 99%

“…Nakano and Kato [23], a PCH classifier is introduced in the training process with a low accuracy rate. The model in [26] can process a large scale of data by using 3rd party API which is not customizable and needs to pay for each time training.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Customer’s spontaneous facial expression recognition

Morshed

Ujir²,

Hipiny³

2021

IJEECS

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<span lang="EN-US">In the field of consumer science, customer facial expression is often categorized either as negative or positive. Customer who portrays negative emotion to a specific product mostly means they reject the product while a customer with positive emotion is more likely to purchase the product. To observe customer emotion, many researchers have studied different perspectives and methodologies to obtain high accuracy results. Conventional neural network (CNN) is used to recognize customer spontaneous facial expressions. This paper aims to recognize customer spontaneous expressions while the customer observed certain products. We have developed a customer service system using a CNN that is trained to detect three types of facial expression, i.e. happy, sad, and neutral. Facial features are extracted together with its histogram of gradient and sliding window. The results are then compared with the existing works and it shows an achievement of 82.9% success rate on average.</span>

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Customer’s spontaneous facial expression recognition

Morshed

Ujir²,

Hipiny³

2021

IJEECS

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Vision-Based Real-Time Hand Wash Accuracy Prediction

Kavinkumar

Geefus

Nair

2021

Intelligent Manufacturing and Energy Sustainability

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Optimizing Convolutional Neural Network Accelerator on Low-Cost FPGA

Vinh

Hai

2021

J CIRCUIT SYST COMP

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Convolutional neural network (CNN) is one of the most promising algorithms that outweighs other traditional methods in terms of accuracy in classification tasks. However, several CNNs, such as VGG, demand a huge computation in convolutional layers. Many accelerators implemented on powerful FPGAs have been introduced to address the problems. In this paper, we present a VGG-based accelerator which is optimized for a low-cost FPGA. In order to optimize the FPGA resource of logic element and memory, we propose a dedicated input buffer that maximizes the data reuse. In addition, we design a low resource processing engine with the optimal number of Multiply Accumulate (MAC) units. In the experiments, we use VGG16 model for inference to evaluate the performance of our accelerator and achieve a throughput of 38.8[Formula: see text]GOPS at a clock speed of 150[Formula: see text]MHz on Intel Cyclone V SX SoC. The experimental results show that our design is better than previous works in terms of resource efficiency.

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Facial Expression Detection Using Neural Network for Customer Based Service

Cited by 3 publications

References 1 publication

Customer’s spontaneous facial expression recognition

Customer’s spontaneous facial expression recognition

Vision-Based Real-Time Hand Wash Accuracy Prediction

Optimizing Convolutional Neural Network Accelerator on Low-Cost FPGA

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