Individual Feature–Appearance for Facial Action Recognition

Dahmane, Mohamed; Meunier, Jean

doi:10.1007/978-3-642-21596-4_24

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Holistic methods are simpler to implement and can handle variations in facial expressions, lighting, and pose without the need for specific feature detection. Techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Gabor wavelets are often applied in these methods to extract relevant features from the pixel data [6].…”

Section: Recommendation Systemmentioning

confidence: 99%

Recommendation system based on facial expression

2023

IRJMETS

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Understanding human emotions relies heavily on interpreting facial expressions, which are a crucial aspect of non-verbal communication. Yet, the intricacies of facial muscle movements make it a formidable task to accurately model these expressions when working with static images, especially when considering different facial poses. To tackle this challenge, we introduce an innovative method that harnesses 3D technology to capture and decode facial expressions more effectively. Moreover, we propose a contour alignment technique designed to enhance modeling precision by autonomously repositioning 3D contour landmarks to align with static images. The acquired input representing facial expressions can be parametrically adjusted, enabling the creation of a diverse range of facial expressions through an expression transfer approach grounded in the Facial Action Coding System (FACS).In an era marked by the explosive growth of the digital landscape and an overwhelming deluge of information, the quest for a reliable method to offer tailored recommendations from this vast sea of data has become an imperative. In response to this challenge, recommendation systems have been engineered. One intriguing avenue involves the utilization of pretrained models such as VGG16, harmoniously amalgamated to forge a tool that could prove immensely advantageous for humanity.We envision that our system will extend its benefits to diverse sectors, encompassing the prediction of films, music selections, and various commodities, among others. The adaptability of this system holds the potential to elevate decision-making processes and furnish invaluable guidance across domains.

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Section: Recommendation Systemmentioning

confidence: 99%

Recommendation system based on facial expression

2023

IRJMETS

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“…The personalized models can be divided into three groups: the person-dependent models ( [8,9,10]), the person-adaptable models ( [11,12,13,14]), and the models that use personalized facial features ( [15]). The first group uses data of both the training and test persons during learning, and these models are typically tailored to each person (training and test).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, [9] used person-specific facial expression data to adapt the Support Vector Machine (SVM) classifier to each individual, with the aim of predicting topical relevance in the context of information search and retrieval. [10] proposed a persondependent graph-fitting method for facial feature tracking, the output of which was used to derive person-dependent facial features for emotion recognition, based on the matching of the personalized facial action graphs. All these approaches achieved better performance in target tasks than generic models; however, separate models needed to be trained for each person.…”

Section: Introductionmentioning

confidence: 99%

Personalized Modeling of Facial Action Unit Intensity

Yang

Rudovic

Pavlović

et al. 2014

Advances in Visual Computing

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Abstract. Facial expressions depend greatly on facial morphology and expressiveness of the observed person. Recent studies have shown great improvement of the personalized over non-personalized models in variety of facial expression related tasks, such as face and emotion recognition. However, in the context of facial action unit (AU) intensity estimation, personalized modeling has been scarcely investigated. In this paper, we propose a two-step approach for personalized modeling of facial AU intensity from spontaneously displayed facial expressions. In the first step, we perform facial feature decomposition using the proposed matrix decomposition algorithm that separates the person's identity from facial expression. These two are then jointly modeled using the framework of Conditional Ordinal Random Fields, resulting in a personalized model for intensity estimation of AUs. Our experimental results show that the proposed personalized model largely outperforms non-personalized models for intensity estimation of AUs.

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