Machine Perception Technologies:

PRODUCTS

Smile Analysis

Anatomy of the Smile. The upper and lower lips frame the display zone of the smile. Within this framework, the components of... 

Fatigue Detection

Driver fatigue detection is determined by monitoring the driver’s grip force on the steering wheel, based on the variation... 

Facial Expression Analysis

Facial expression communicates information about emotions, regulates interpersonal behavior and person perception, indexes physiologic...

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NEWS

We launched SMILE for Android

We just launched our first version of SMILE for Android. It detects smiles, ranks them and puts them in a world map. You can get it for $0.99 at the Google Android Store. (Just type Smile for Android on Google).
NOTE: No products are available.

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Some Background on Facial Recognition Technology

HUMAN LIE DETECTOR PAUL EKMAN DECODES THE FACES OF DEPRESSION, TERRORISM, AND JOY

Just imagine if expert humans or face-reading machines were at the airports where the 9/11 hijackers had boarded their doomed planes. Perhaps thousands of lives would have been saved on 9/11 if the emotional states of hijackers had been correctly deducted.According to says San Francisco-based psychologist Paul Ekman detainments would have been triggered. Unfortunately there were no expert humans or face-reading machines being used.

Tim Roth’s character in the Fox TV series Lie To Me was based on Paul Ekman and his work. Since 9/11, Ekman has worked with the Central Intelligence Agency (CIA), the Department of Homeland Security (DHS), the Department of Defense (DOD), among others to help train people, and develop machines that can read faces for emotions with the goal of preventing disastrous events occurring at all levels.

Additional facial recognition applications could include assisting Transportation Security Administration (TSA) agents screen for potential terrorists at airports, or teaching U.S. Army Special Forces in Afghanistan and elsewhere how to determine an enemy combatant’s veracity or his/ her intent to kill. Ekman slap has provided training to a number of guards at Abu Ghraib prison.  By using his facial analysis work, guards were better able to extract information without the use of torture.

Paul Ekman was a pioneer in the field of facial emotion measurement and the neuro-scientific areas, which overlaps with both face-recognition and neuro-marketing. Along with the Dalai Lama, Ekman has written a book, Emotional Awareness. Universally credited with developing the Facial Action Coding System (FACS). FACS is the comprehensive dictionary of facial expression measurements. It has become the scientific underpinning for human observation and automated facial analysis internationally as well as across a variety of academic and commercial fields. For instance, both Microsoft and Apple are building their own facial recognition capabilities, as has Google. In some school districts his methodology is being put to work particularly for better understanding those with cognitive disabilities. And it can enable people to be more in tune with their emotions.

According to Ekman there are two basic ways to measure facial emotion. You can use specially trained people to analyze facial micro-expressions and emotions or use a technological, automated method. He prefers using the human approach when time is of the essence such as in life and death instances involving and law enforcement officers, intelligence, or military situations. He believes it is significantly more accurate than relying on an automated method.  However, one of the fallacies of using humans is that they can become fatigued. In addition, their levels of observation or interpretation can vary considerably. However, using the technology for laborious, frame-by-frame video analysis also has its place, particularly as a backup system.

Ekman, presently sits on the board of a nascent company, which specializes in automating facial expressions analysis based on his FACS foundation. The CIA has been studying the efficacy of the automating micro-expressions analysis methodology of Machine Perception Technologies (MPT) "Neural Network" versus other alternative methods. MPT's splits it work between the "security" arena and marketing for such companies as Procter & Gamble, Intel, and Sony. One of the main goals of MPT is in advancing "machine learning" and creating smarter, more natural interaction and interfaces between humans and machines.

Affectiva, another company whose methodology is based on Ekman’s FACS advertises itself as an emotion measurement technology company. Affectiva's initial focus has been in health care. They have developed tools, which help people on the autism spectrum to communicate as well as applications that have developed tools that help those on the autism spectrum to communicate and applications, via skin and facial sensors that allow people to self-monitor their anxiety level or heart rate. Following the money, Affectiva has been busy consulting marketing and media clients regarding their advertising and consumer engagement. However, according to their COO, Affectiva will never focus on security and deception. Instead they want people to be more aware of their emotions and empowered to better manage and improve their moods, health, work, socializing and life. They say their focus is on doing good in the world with the broadest applications.

There are critics of this field that encompasses facial expression measurements via both human and technology. They feel it opens the doors to big brother intrusions, sinister mind reading, and phony junk science with low accuracy, and/or high expenses.

Even though there are differences in their methods, services and views, all of the facial emotion measurement experts their companies agree that new applications will emerge that cannot even be imagined today, but will forever change the way we do things in the future.

 

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More Background on MPT4U.com: A Pioneer in Machine Perception Technologies

Introduction

MPT4U.com, once the digital face of Machine Perception Technologies, has its roots in the groundbreaking and rapidly evolving field of facial recognition technology. Established in 2009 by Javier R. Movellan, Stanley Kim, and Gwen Littlewort Ford, this San Diego-based company focused on developing innovative applications that could interpret human facial expressions through computational methods. Although the company's domain only remained active for three years, its influence in the realm of machine perception continues to be recognized.

History and Founding

Machine Perception Technologies (MPT) was founded with a mission to bridge the gap between human sensory perception and machine learning. This niche area of artificial intelligence aimed to create systems that could understand and process human expressions in ways previously thought to be the exclusive domain of humans. The company's primary focus was on facial expression analysis, a technology that could have far-reaching implications in various industries, including security, healthcare, and marketing.

The company emerged during a time when facial recognition technology was still in its formative years but was gaining traction due to advances in computational power and algorithmic development. The roots of facial recognition technology can be traced back to the 1960s, with early pioneers like Woodrow Wilson Bledsoe, who developed a system for manually classifying facial features. Over the decades, this technology evolved, particularly with the introduction of the Eigenface approach in the late 1980s, which provided a more automated and scalable method for facial recognition.

Technological Innovations and Products

MPT's innovation lay in its ability to capture and analyze facial expressions through a computational framework. One of its key products was a smile analysis tool, which quantified and analyzed smiles to provide insights into emotional states. This was part of a broader suite of tools aimed at understanding human emotions, which included fatigue detection systems designed to monitor driver fatigue by analyzing grip force on the steering wheel, and comprehensive facial expression analysis systems that could detect and interpret a wide range of emotional cues.

These technologies were underpinned by advancements in machine learning and neural networks, which allowed MPT's systems to learn from vast datasets of facial expressions and improve their accuracy over time. The company's work in this area was aligned with the broader trends in facial recognition technology, which saw significant improvements in the 2010s due to the development of deep learning algorithms.

Press and Media Coverage

During its operational years, MPT received attention from various media outlets for its cutting-edge work. The company's focus on developing applications that could potentially be used in security and marketing attracted interest from both commercial entities and government agencies. Notably, MPT's technology was of interest to the CIA, which explored the efficacy of its facial expression analysis systems for security applications. This highlights the dual-use nature of the technology, which could be applied both for consumer insights in marketing and for critical security operations.

Audience and Cultural Significance

MPT's audience was diverse, encompassing both the academic community interested in the theoretical underpinnings of facial recognition and commercial entities looking for practical applications. The technology developed by MPT had the potential to revolutionize industries by providing a more nuanced understanding of human emotions. For instance, in marketing, this could translate to more effective advertising by tailoring messages based on real-time emotional feedback. In security, it could enhance surveillance systems by allowing for the detection of suspicious behavior based on facial expressions.

Culturally, MPT's work sits at the intersection of technology and ethics. As facial recognition technology has become more pervasive, it has sparked debates about privacy and the potential for misuse. The ability to read and interpret facial expressions through automated systems raises questions about surveillance and the boundaries of acceptable use. While MPT's technology promised significant benefits, it also underscored the need for ethical considerations in the deployment of such systems.

Challenges and Criticisms

Despite its innovations, the field of facial recognition has not been without its critics. Concerns about privacy, accuracy, and the potential for misuse have been central to the debate. Critics argue that while facial recognition can be a powerful tool, it also opens the door to "big brother" scenarios where individuals' every expression could be monitored and analyzed without their consent. Additionally, there is ongoing debate about the accuracy of these systems, particularly in diverse populations where facial recognition algorithms have been shown to have higher error rates.

MPT, like other companies in this field, had to navigate these challenges while pushing the boundaries of what was technologically possible. The ethical implications of their work remain a topic of discussion, particularly as facial recognition technology becomes more integrated into daily life.

 

MPT4U.com was the digital representation of a company at the forefront of a technological revolution. Machine Perception Technologies contributed to the early development of facial recognition systems that are now ubiquitous in various sectors. While the company itself may no longer be active, its legacy lives on in the ongoing evolution of machine perception and the broader discussions about the role of such technologies in society.

As facial recognition technology continues to advance, the work pioneered by companies like MPT will remain relevant, serving as both a foundation for future developments and a cautionary tale about the need for careful consideration of the ethical implications of such powerful tools.