Real time facial expression recognition app development on mobile phones

Alshamsi, Humaid; Meng, Hongying; Li, Maozhen

doi:10.1109/fskd.2016.7603442

Cited by 22 publications

(10 citation statements)

References 15 publications

(18 reference statements)

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“…The current state of the art-in terms of emotional inference devices-consists of deep learning models, often implemented in phone apps using cameras (Alshamsi et al, 2016). To train these agents, an enormous amount of visual information is collected, and the agent is trained on which emotions the photographs display.…”

Section: Wave Onementioning

confidence: 99%

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An Investigation of the Free Energy Principle for Emotion Recognition

Daphne

Parr

Friston

2020

Front. Comput. Neurosci.

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This paper offers a prospectus of what might be achievable in the development of emotional recognition devices. It provides a conceptual overview of the free energy principle; including Markov blankets, active inference, and-in particular-a discussion of selfhood and theory of mind, followed by a brief explanation of how these concepts can explain both neural and cultural models of emotional inference. The underlying hypothesis is that emotion recognition and inference devices will evolve from state-of-the-art deep learning models into active inference schemes that go beyond marketing applications and become adjunct to psychiatric practice. Specifically, this paper proposes that a second wave of emotion recognition devices will be equipped with an emotional lexicon (or the ability to epistemically search for one), allowing the device to resolve uncertainty about emotional states by actively eliciting responses from the user and learning from these responses. Following this, a third wave of emotional devices will converge upon the user's generative model, resulting in the machine and human engaging in a reciprocal, prosocial emotional interaction, i.e., sharing a generative model of emotional states.

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Section: Wave Onementioning

confidence: 99%

“…To train these agents, an enormous amount of visual information is collected, and the agent is trained on which emotions the photographs display. Over time, the agent begins to recognize patterns within this visual information-and attempts to infer emotions accordingly (Alshamsi et al, 2016).…”

Section: Wave Onementioning

confidence: 99%

An Investigation of the Free Energy Principle for Emotion Recognition

Daphne

Parr

Friston

2020

Front. Comput. Neurosci.

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“…It could be integrated with voice recognition systems [37], facial recognition [38], and fingerprint recognition [39].…”

Section: Extension Of Firewall Capabilitiesmentioning

confidence: 99%

A Cryptographic System Based upon the Principles of Gene Expression

Shaw

2017

Cryptography

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Processes of gene expression such as regulation of transcription by the general transcription complex can be used to create hard cryptographic protocols which should not be breakable by common cipherattack methodologies. The eukaryotic processes of gene expression permit expansion of DNA cryptography into complex networks of transcriptional and translational coding interactions. I describe a method of coding messages into genes and their regulatory sequences, transcription products, regulatory protein complexes, transcription proteins, translation proteins and other required sequences. These codes then serve as the basis for a cryptographic model based on the processes of gene expression. The protocol provides a hierarchal structure that extends from the initial coding of a message into a DNA code (ciphergene), through transcription and ultimately translation into a protein code (cipherprotein). The security is based upon unique knowledge of the DNA coding process, all of the regulatory codes required for expression, and their interactions. This results in a set of cryptographic protocols that is capable of securing data at rest, data in motion and providing an evolvable form of security between two or more parties. The conclusion is that implementation of these protocols will enhance security and substantially burden cyberattackers to develop new forms of countermeasures.

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“…Finally, the entire project was tested to guarantee that the project met the requirements and achieved its aim. This is the most significant step, which ensures that the study algorithm is efficient and powerful; that it could recognize emotional face expressions from a mobile phone and it is much better than previous work that been test before by using BRIEF method [45].…”

Section: Testing Summarymentioning

confidence: 99%

Real-Time Facial Expression Recognition App Development on Smart Phones

Alshamsi¹,

Kupuska²

2017

IJERA

Self Cite

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Facial expression recognition (FER) is a crucial technology and a challenging task for human computer interaction. Previous method has been using different feature and classification method for FER and use basic method for testing. In this paper, we used the best feature descriptor for FER by empirically evaluating two feature descriptors, namely Facial Landmarks, and Binary Robust Independent Elementary Features (BRIEF) descriptors. We examine each feature descriptor by considering one classification method such as Support Vector Machine (SVM) with three unique facial expression recognition datasets. In addition to test accuracies, we present confusion matrixes of FER. We also analyze the effect of using this feature and image resolutions on FER performance. Our study indicates that Facial Landmarks descriptor works the best for FER to run on smart phone. The experimental results demonstrate that the proposed facial expression recognition on mobile phone is successful and it gives up to 96.27 % recognition accuracy.

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Real time facial expression recognition app development on mobile phones

Cited by 22 publications

References 15 publications

An Investigation of the Free Energy Principle for Emotion Recognition

An Investigation of the Free Energy Principle for Emotion Recognition

A Cryptographic System Based upon the Principles of Gene Expression

Real-Time Facial Expression Recognition App Development on Smart Phones

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