Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung, Ervin Gubin

doi:10.30534/ijatcse/2020/1691.22020

Cited by 8 publications

(8 citation statements)

References 50 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, Table 5 presented the performance of the single classifiers with average accuracy sorted from best to worst. The average accuracy is defined as in (2).…”

Section: Experiments With a Single Classifier On The Fer-2013 Datasetmentioning

confidence: 99%

“…SA based on textual data from various social media sources provides a simple way for businesses to gather customer feedback and further develop their products based on current market demands or trends. Machine learning has been used as a data processing technique to solve a wide range of problems in a variety of fields, including face recognition [2]- [4] and facial expression recognition. The facial expression is the most noticeable expression to distinguish a human emotion.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble-based face expression recognition approach for image sentiment analysis

Moung

Wooi

Sufian

et al. 2022

IJECE

View full text Add to dashboard Cite

<span>Sentiment analysis based on images is an evolving area of study. Developing a reliable facial expression recognition (FER) device remains a difficult challenge as recognizing emotional feelings reflected in an image is dependent on a diverse set of factors. This paper presented an ensemble-based model for FER that incorporates multiple classification models: i) customized convolutional neural network (CNN), ii) ResNet50, and iii) InceptionV3. The model averaging ensemble classifier method is used to ensemble the predictions from the three models. Subsequently, the proposed FER model is trained and tested on a dataset with an uncontrolled environment (FER-2013 dataset). The experiment demonstrated that ensembling multiple classifiers outperformed all single classifiers in classifying positive and neutral expressions (91.7%, 81.7% and 76.5% accuracy rate for happy, surprise, and neutral, respectively). However, when classifying disgust, anger, and sadness, the ResNet50 model alone is the better choice. Although the Custom CNN performs the best in classifying fear expression (55.7% accuracy), the proposed FER model can still classify fear expression with comparable performance (52.8% accuracy). This paper demonstrated the potential of using the ensemble-based method to enhance the performance of FER. As a result, the proposed FER model has shown a 72.3% accuracy rate.</span>

show abstract

“…On the other hand, Table 5 presented the performance of the single classifiers with average accuracy sorted from best to worst. The average accuracy is defined as in (2).…”

Section: Experiments With a Single Classifier On The Fer-2013 Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble-based face expression recognition approach for image sentiment analysis

Moung

Wooi

Sufian

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…Machine learning is used as a data processing technique to solve a wide range of problems in a variety of fields, including smart homes [11], human identification in healthcare [12], face recognition [13][14][15], water quality research [16], and many more. In traditional machine learning, tedious and exhaustive feature extraction is a very common practice in order to produce a highly discriminative feature.…”

Section: Related Workmentioning

confidence: 99%

Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business Analytics

et al. 2021

Self Cite

View full text Add to dashboard Cite

In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance.

show abstract

“…In research topics such as face recognition [31] performance is straightforward to assess, making it easy to review methods and say which is best. For a number of reasons, the same is not true for evaluating designs, and this is acknowledged as a significant issue within design studies [17,24].…”

Section: Performance and Evaluationmentioning

confidence: 99%

Progress and Challenges in Generative Product Design: A Review of Systems

Mountstephens

Teo

2020

Computers

View full text Add to dashboard Cite

Design is a challenging task that is crucial to all product development. Advances in design computing may allow machines to move from a supporting role to generators of design content. Generative Design systems produce designs by algorithms and offer the potential for the exploration of vast design spaces, the fostering of creativity, the combination of objective and subjective requirements, and the revolutionary integration of conceptual and detailed design phases. The application of generative methods to the design of discrete, physical, engineered products has not yet been reviewed. This paper reviews the Generative Product Design systems developed since 1998 in order to identify significant approaches and trends. Systems are analyzed according to their primary goal, generative method, the design phase they focus on, whether the generation is automatic or interactive, the number of design options they generate, and the types of design requirements involved in the generation process. Progress using this approach is recognized, and a number of challenges that must be addressed in order to achieve widespread acceptance are identified. Possible solutions are offered, including innovative approaches in Human–Computer Interaction.

show abstract

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Cited by 8 publications

References 50 publications

Ensemble-based face expression recognition approach for image sentiment analysis

Ensemble-based face expression recognition approach for image sentiment analysis

Indigenous Food Recognition Model Based on Various Convolutional Neural Network Architectures for Gastronomic Tourism Business Analytics

Progress and Challenges in Generative Product Design: A Review of Systems

Contact Info

Product

Resources

About