A Preliminary Work on Dog Emotion Recognition

Franzoni, Valentina; Milani, Alfredo; Biondi, Giulio; Micheli, F.

doi:10.1145/3358695.3361750

Cited by 19 publications

(29 citation statements)

References 33 publications

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“…Macaques emotion induced behavior face -Blumrosen et al [86] induced behavior face -Zhu [87] Dogs pain naturally occuring body + Franzoni et al [88] emotion unknown face + Boneh-Shitrit et al [89] induced behavior face + Ferres et al [90] unknown body +…”

Section: Morozov Et Al [85]mentioning

confidence: 99%

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Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

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Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of affective states and pain in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic -classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

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Section: Morozov Et Al [85]mentioning

confidence: 99%

“…Cases when data is scraped from the Internet [88,90] should be treated with caution in this context (and are accordingly labeled as 'unknown' in Table 1), as the degree of control cannot be asserted. When the state is not controlled, the only available option for establishing ground truth is by human annotators.…”

Section: Data Annotationmentioning

confidence: 99%

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Lopes et al [32] proposed several pre-processing steps before feeding the faces to a convolutional neural network for facial expression recognition. Franzoni et al [33] took a step toward animal welfare and proposed a technique for recognizing the emotions of dogs. The proposed method has potential applications in supportive systems for pets.…”

Section: Visual Signal Based Emotion Classificationmentioning

confidence: 99%

Facial Emotion Recognition Using Hybrid Features

Alreshidi

Ullah

2020

Informatics

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Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively.

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“…Visual transfer learning is employed to benefit from both the vast, existing visual knowledge base and the fast training time required. Convolutional Neural Networks trained with extensive image datasets, such as the popular ImageNet, have been proved to embed excellent recognition abilities [13,18,24]. AlexNet, trained on ImageNet, is one of the first of such CNN and is still among the most widely used for deep learning-based image classification.…”

Section: Domain-specific Training Of the Alexnet Cnnmentioning

confidence: 99%

Emotional sounds of crowds: spectrogram-based analysis using deep learning

Franzoni

Biondi

Milani

2020

Multimed Tools Appl

Self Cite

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Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. A critical question concerning the innovative concept of crowd emotions is whether the emotional content of crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations. In this work, we present a technique based on the generation of sound spectrograms from fragments of fixed length, extracted from original audio clips recorded in high-attendance events, where the crowd acts as a collective individual. Transfer learning techniques are used on a convolutional neural network, pre-trained on low-level features using the well-known ImageNet extensive dataset of visual knowledge. The original sound clips are filtered and normalized in amplitude for a correct spectrogram generation, on which we fine-tune the domain-specific features. Experiments held on the finally trained Convolutional Neural Network show promising performances of the proposed model to classify the emotions of the crowd.

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A Preliminary Work on Dog Emotion Recognition

Cited by 19 publications

References 33 publications

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Facial Emotion Recognition Using Hybrid Features

Emotional sounds of crowds: spectrogram-based analysis using deep learning

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