Biometrics for Surveillance

Yan, Wei Qi

doi:10.1007/978-3-319-60228-8_5

Cited by 1 publication

(2 citation statements)

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also important to understand that several algorithms pose limitations. As an example, the Viola-Jones algorithm can be used along with Haar feature selection and AdaBoost training algorithm for remarkable detection of the eye region and nose bridge region with the limitation that it is only effective for the frontal images and can hardly cope with a 45∘ face rotation [61].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Also, multiple intensities are measured with a value of rank one recognition and rank five recognition, where rank one is that the intensity is measured at the highest accuracy level and rank five at the lowest. Algorithms that have been used for feature extraction span from classical techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) to modern techniques such as machine learning (ML) and artificial neural networks (ANN) [10,11,12,13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Mehta

Siddiqui

Javaid

2019

Sensors

View full text Add to dashboard Cite

Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.

show abstract