Facial Recognition - Revision history

BPeat: /* Emotion Recognition */

2023-12-15T03:00:45Z

‎Emotion Recognition

BPeat: /* Emotion Recognition */

2023-12-15T02:59:54Z

‎Emotion Recognition

BPeat: /* Emotion Recognition */

2023-12-15T02:48:26Z

‎Emotion Recognition

BPeat: /* Emotion Recognition */

2023-12-15T02:44:21Z

‎Emotion Recognition

BPeat: /* Emotion Recognition */

2023-12-15T02:43:41Z

‎Emotion Recognition

BPeat at 02:33, 15 December 2023

2023-12-15T02:33:08Z

BPeat: /* Emotion Recognition */

2023-12-15T02:29:44Z

‎Emotion Recognition

BPeat at 01:04, 15 December 2023

2023-12-15T01:04:07Z

BPeat at 10:10, 5 July 2023

2023-07-05T10:10:31Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T17:17:18Z

Text replacement - "http:" to "https:"

Show changes

← Older revision		Revision as of 03:00, 15 December 2023
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	* <b>Confusion</b>: The confusion slider reflects the clarity of the emotional experience. Users can slide towards "Clear" if they feel no confusion or towards "Confused" to indicate a high level of confusion associated with the emotion.		* <b>Confusion</b>: The confusion slider reflects the clarity of the emotional experience. Users can slide towards "Clear" if they feel no confusion or towards "Confused" to indicate a high level of confusion associated with the emotion.
		+

	<youtube>5ALNG6fm4XQ</youtube>		<youtube>5ALNG6fm4XQ</youtube>

← Older revision		Revision as of 02:59, 15 December 2023
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	** Healthcare: Emotion recognition has applications in mental health monitoring and assisting individuals with conditions like autism or depression.		** Healthcare: Emotion recognition has applications in mental health monitoring and assisting individuals with conditions like autism or depression.

		+
		+	<i>Emotion dimensions</i>:
		+
		+	* <b>Valence</b>: This slider captures the positive or negative aspect of emotions. Users can slide towards positive for emotions like happiness and contentment or towards negative for emotions like sadness and frustration.
		+
		+	* <b>Arousal</b>: Arousal reflects the intensity or activation level of emotions. Users can adjust the slider to express how calm or excited they feel. Low arousal might represent calmness, while high arousal could signify excitement or agitation.
		+
		+	* <b>Intensity</b>: This slider measures the overall strength or magnitude of the emotion. Users can adjust it to convey whether their emotional experience is more subtle and mild or strong and intense.
		+
		+	* <b>Dominance</b>: Dominance indicates the perceived level of control or influence in the emotional state. A slider can help users express whether they feel more submissive or dominant in a given emotional moment.
		+
		+	* <b>Surprise</b>: The surprise slider allows users to convey the unexpectedness of their emotional experience. It ranges from no surprise to extreme surprise, capturing the element of novelty in the emotional response.
		+
		+	* <b>Sadness</b>: This slider gauges the depth of sadness. Users can slide towards "None" for emotions with minimal sadness and towards "Overwhelming" for emotions associated with profound sadness.
		+
		+	* <b>Anger</b>: The anger slider enables users to express the intensity of their anger. It spans from no anger to extreme fury, providing a spectrum for users to convey the strength of their emotional response.
		+
		+	* <b>Joy/Happiness</b>: Users can use this slider to indicate the level of happiness or joy they are experiencing. It ranges from no joy to an ecstatic level of joy, helping to capture the range of positive emotional experiences.
		+
		+	* <b>Fear</b>: Fear intensity is expressed through this slider. Users can convey whether they feel a slight unease or are experiencing a state of extreme terror or fear.
		+
		+	* <b>Disgust</b>: This slider measures the intensity of disgust. Users can slide towards "None" for minimal disgust or towards "Repulsed" for emotions associated with strong feelings of disgust.
		+
		+	* <b>Interest/Engagement</b>: Users can adjust this slider to express their level of interest or engagement. It ranges from being uninterested or disengaged to fully engaged or absorbed in the experience.
		+
		+	* <b>Confusion</b>: The confusion slider reflects the clarity of the emotional experience. Users can slide towards "Clear" if they feel no confusion or towards "Confused" to indicate a high level of confusion associated with the emotion.

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@@ Line 123: / Line 123: @@
 * <b>Facial Recognition</b>:
-** Facial Expression Analysis: AI algorithms analyze facial features and expressions to detect emotions. Deep learning techniques, such as convolutional neural networks (CNNs), are commonly used for this purpose.
+** Facial Expression Analysis: AI algorithms analyze facial features and expressions to detect emotions. Deep learning techniques, such as [[(Deep) Convolutional Neural Network (DCNN/CNN)]], are commonly used for this purpose.
 ** Facial Landmark Detection: Algorithms identify key points on the face, such as the position of the eyes, nose, and mouth, to understand facial expressions and infer emotions.
-* <b>Speech Recognition</b>: Voice Analysis: AI systems analyze speech patterns, tone, pitch, and other acoustic features to detect emotions in spoken language. Natural language processing (NLP) techniques are often employed for this task.
+* <b>[[Speech Recognition|Speech Recognition]]</b>: [[Speech Recognition|Voice Analysis]]: AI systems analyze speech patterns, tone, pitch, and other acoustic features to detect emotions in spoken language. [[Natural Language Processing (NLP)]] techniques are often employed for this task.
 * <b>Gesture Recognition</b>: Body Language Analysis: AI can be trained to recognize specific gestures, body movements, and postures that are associated with different emotions.
 * <b>Biometric Sensors</b>:: Physiological Signals: Some systems use biometric sensors to measure physiological signals such as heart rate, skin conductance, and EEG signals. Changes in these signals can be indicative of emotional states.

@@ Line 130: / Line 130: @@
 * <b>Multimodal Approaches</b>: Combining Multiple Modalities: Emotion recognition systems often integrate information from multiple sources, such as facial expressions, speech, and gestures, to improve accuracy and reliability.
 * <b>Machine Learning and Deep Learning</b>: Training Models: AI models, particularly machine learning and deep learning models, are trained on large datasets containing labeled examples of different emotional states. These models learn patterns and features associated with specific emotions.
-* <b>Real-Time Applications</b>:
+* <b>Real-Time Applications</b>: Live Interaction Analysis: Some applications use AI to analyze emotions in real-time, enabling adaptive responses in human-computer interaction scenarios, such as virtual assistants responding to user emotions.
 * <b>Applications in Various Fields</b>:
 ** Customer Service: Emotion recognition is applied in customer service to gauge customer satisfaction and provide personalized responses.

← Older revision		Revision as of 02:43, 15 December 2023
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	This project was created by a group of social scientists, citizen scientists, and designers. We want to open up conversations about emotion recognition systems from the science behind the technology to their social impacts--and everything else in between. Our aim is to promote public understanding of these technologies and citizen involvement in their [[development]] and use. We believe that through collective intelligence and sharing perspectives on such important issues, we can empower communities to promote a just and equitable society.		This project was created by a group of social scientists, citizen scientists, and designers. We want to open up conversations about emotion recognition systems from the science behind the technology to their social impacts--and everything else in between. Our aim is to promote public understanding of these technologies and citizen involvement in their [[development]] and use. We believe that through collective intelligence and sharing perspectives on such important issues, we can empower communities to promote a just and equitable society.
		+
		+
		+	AI is used for emotion recognition through the application of various techniques and technologies. Emotion recognition aims to identify and understand human emotions based on facial expressions, speech patterns, gestures, and other physiological signals. Here are some ways in which AI is employed for emotion recognition:
		+
		+	* <b>Facial Recognition</b>:
		+	** Facial Expression Analysis: AI algorithms analyze facial features and expressions to detect emotions. Deep learning techniques, such as convolutional neural networks (CNNs), are commonly used for this purpose.
		+	** Facial Landmark Detection: Algorithms identify key points on the face, such as the position of the eyes, nose, and mouth, to understand facial expressions and infer emotions.
		+	* <b>Speech Recognition</b>: Voice Analysis: AI systems analyze speech patterns, tone, pitch, and other acoustic features to detect emotions in spoken language. Natural language processing (NLP) techniques are often employed for this task.
		+	* <b>Gesture Recognition</b>: Body Language Analysis: AI can be trained to recognize specific gestures, body movements, and postures that are associated with different emotions.
		+	* <b>Biometric Sensors</b>:: Physiological Signals: Some systems use biometric sensors to measure physiological signals such as heart rate, skin conductance, and EEG signals. Changes in these signals can be indicative of emotional states.
		+	* <b>Multimodal Approaches</b>: Combining Multiple Modalities: Emotion recognition systems often integrate information from multiple sources, such as facial expressions, speech, and gestures, to improve accuracy and reliability.
		+	* <b>Machine Learning and Deep Learning</b>: Training Models: AI models, particularly machine learning and deep learning models, are trained on large datasets containing labeled examples of different emotional states. These models learn patterns and features associated with specific emotions.
		+	* <b>Real-Time Applications</b>:
		+	Live Interaction Analysis: Some applications use AI to analyze emotions in real-time, enabling adaptive responses in human-computer interaction scenarios, such as virtual assistants responding to user emotions.
		+	* <b>Applications in Various Fields</b>:
		+	** Customer Service: Emotion recognition is applied in customer service to gauge customer satisfaction and provide personalized responses.
		+	** Education: AI-based emotion recognition is used in educational technology to adapt teaching methods based on students' emotional states.
		+	** Healthcare: Emotion recognition has applications in mental health monitoring and assisting individuals with conditions like autism or depression.
		+

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← Older revision		Revision as of 02:33, 15 December 2023
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	This project was created by a group of social scientists, citizen scientists, and designers. We want to open up conversations about emotion recognition systems from the science behind the technology to their social impacts--and everything else in between. Our aim is to promote public understanding of these technologies and citizen involvement in their [[development]] and use. We believe that through collective intelligence and sharing perspectives on such important issues, we can empower communities to promote a just and equitable society.		This project was created by a group of social scientists, citizen scientists, and designers. We want to open up conversations about emotion recognition systems from the science behind the technology to their social impacts--and everything else in between. Our aim is to promote public understanding of these technologies and citizen involvement in their [[development]] and use. We believe that through collective intelligence and sharing perspectives on such important issues, we can empower communities to promote a just and equitable society.
		+
		+	<youtube>5ALNG6fm4XQ</youtube>
		+	<youtube>QZQwKa1sBlY</youtube>

	= Pets =		= Pets =

@@ Line 115: / Line 115: @@
 * [https://emojify.info/menu Emotion Recognition Sandbox | Nesta]
 * [https://emojify.info/resources#resources Resources | Nesta]
 This project was created by a group of social scientists, citizen scientists, and designers. We want to open up conversations about emotion recognition systems from the science behind the technology to their social impacts--and everything else in between. Our aim is to promote public understanding of these technologies and citizen involvement in their [[development]] and use. We believe that through collective intelligence and sharing perspectives on such important issues, we can empower communities to promote a just and equitable society.
 = Pets =

@@ Line 108: / Line 108: @@
 * Face Authentication (1:1 Matching+Liveness), on the other hand, takes User-initiated data collected from a device they do control and confirms that User's identity for their own direct benefit, like, for example, secure account access.
-<b>Emotion Recognition</b>
+= Emotion Recognition =
 [https://www.youtube.com/results?search_query=Emotion+facial+Recognition Youtube search...]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, Facebook
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=facial+face+recognition+deep+machine+learning+ML Youtube search...]
 [https://www.google.com/search?q=facial+face+recognition+deep+machine+learning+ML ...Google search]
-* [[Screening; Passenger, Luggage, & Cargo]]
+* [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]]
 * [https://www.nist.gov/programs-projects/face-projects Face | National Institute of Standards and Technology (NIST)]
 * [https://arxiv.org/ftp/arxiv/papers/1907/1907.12739.pdf Deep Learning For Face Recognition: A Critical Analysis | Andrew Jason Shepley]