MATAMATAMATICSMATICSARTIFICIAL INTELLIGENCE, MATHEMATICS AND MAKEUP ARTAng matang MAKEUP LOOKS SHOWCASED 3 sumisilip sa hinaharap. Discover how mathematics, patterns, pixels shape facial recognition and how makeup can deceive the algorithms that see us. 04 NOVEMBER 2026 VOL. 01

The History of Facial RecognitionFacial recognition technology began in the 1960s when mathematician Woodrow Wilson Bledsoe developed a system that used electromagnetic coordinates to measure facial features. With the help of Helen Chan Wolf and Charles Bisson, the system compared measurements such as the distance between the eyes to identify individuals. Over time, the technology advanced through methods such as Eigenfaces in 1991 and the Viola- Jones algorithm in 2001, which made facial recognition faster and more practical. Now In the 21 century, facial recognition is mostly used for security systems in airport, stores, and law enforcement tracking. st Source: Rafael Campillo, Mobbeel.ANG SIMULA

CV DAZZLEContourPatternsLight Color BlockingDark Color BlockingHarvey's original designs targeted the Viola–Jones face detection algorithm, which relied on specific patterns of light and dark to locate faces. Harvey's original designs targeted the Viola–Jones face detection algorithm, which relied on specific patterns of light and dark to locate faces. By using unconventional makeup, contrasting colors, and asymmetrical hairstyles, CV Dazzle could disrupt these expected patterns and prevent certain versions of the algorithm from detecting a face. According to Caracomp (2026), the process in anti- facial recognition makeup is through contour mapping, color blocking and feature disruption of the face, the AI system algorithm is then confused and detection of the face is prevented.Our project is inspired by CV Dazzle, short for Computer Vision Dazzle. Developed by artist and researcher Adam Harvey in 2010 as part of his master's thesis at Interactive Telecommunications Program, CV Dazzle explores how fashion can be used to interfere with computer vision systems. Unlike traditional camouflage, which conceals someone from human observers, CV Dazzle is designed to disrupt machine vision.Source of two photos above and text info: CV Dazzle by Adam Harvey.

1) Creating Digital Fingerprints. Each pixel is assigned a brightness value from 0 to 255, which the algorithm compares with its eight neighboring pixels to create a unique numerical pattern.MATEMATIKABehind Anti-Facial Recognition MakeupMATEMATIKABehind Anti-Facial Recognition MakeupThe algorithm converts the face into a black-and-white image, making it easier to identify patterns based on how light or dark each pixel is. 2) The algorithm converts the eight comparisons into a sequence of zeros and ones, then turns that binary number into a decimal value representing the local pattern. 3) The algorithm repeats this process across the image to capture the local patterns found throughout the face. Then counts the occurrences of each pattern, recording how frequently each numerical value appears in the image.Source of Information and Visuals: CS Coach. (2024, November 29). How Facial Recognition Works | Local Binary Patterns Explained [Video]. YouTube.

MATEMATIKABehind Anti-Facial Recognition Makeup4) The algorithm organizes these counts into a histogram, creating a digital fingerprint that summarizes the patterns found in the face.5) Using the Chi-square formula, the algorithm measures the differences between the pattern counts in two facial images.6) For example, if two facial images have different head shapes, their patterns may differ more, resulting in a greater distance between their histograms.7) When comparing real human faces, the algorithm measures the distance between their histograms: a smaller distance suggests a possible match, while a larger distance suggests that the faces are less likely to belong to the same person.Source of Information and Visuals: CS Coach. (2024, November 29). How Facial Recognition Works | Local Binary Patterns Explained [Video]. YouTube.

NAKIKITA MO BA? Let’s take a break from all the reading. here’s a little game for you, try to find the 10 hidden objects from the illustration below. Mas madaling maghanap kung gagamitin ang mga mata.

Have you ever wondered what happens to the photos we upload online? You upload a photo to Facebook, Instagram, Twitter, Threads or TikTok, expecting to share a memory or simply just for fun. In a 2019 NBC News investigation, journalist Olivia Solon reported how millions of photographs uploaded online had been collected and used to develop facial recognition technology. One example was IBM's Diversity in Faces dataset, which contained nearly one million photographs originally obtained from Flickr. These images were annotated with information about facial features, skin tone, estimated age, and other characteristics to help researchers study and improve facial recognition systems. According to IBM, the goal was to help make facial recognition fairer across different demographic groups. However, the investigation found that some photographers were surprised to learn that their images had been included in the dataset.STANCE ON AIMagnanakaw nga ba ng mukha?This brings us to the issue of consent. Sharing something online does not necessarily mean agreeing to have your face used to develop biometric technology. Here in the Philippines, the Data Privacy Act of 2012, or Republic Act No. 10173, provides legal protections for personal information. Specifically, Section 11 requires personal data to be processed according to the principles of transparency, legitimate purpose, and proportionality. data to develop AI systems must have a lawful basis and comply with the law's data privacy principlesSolon, O. (2019, March 12). Facial recognition’s ‘dirty little secret’: Millions of online photos scraped without consent. NBC News

MATA-MATICS LOOKBOOKMata-Matics Look no.1 by Shane Lagaña Mata-Matics Look no.2 by Sam TudThis lookbook features three makeup looks created using techniques inspired by Adam Harvey’s CV Dazzle. The looks were achieved using face paint, allowing students to experiment with different colors and shapes.

Mata-Matics Look no.3 by Francis OrtizAfter completing the three makeup looks, we tested each one using the facial recognition feature on our phones. Unfortunately, Looks 1 and 2 were still recognized, as the phones successfully unlocked upon detection. However, Look 3, created by Francis Ortiz, proved to be the most effective. When tested, the phone failed to recognize the face and remained locked, suggesting that the makeup was more successful in disrupting facial recognition. TESTING

MATA-MATICS: Ang matang sumisilip sa hinaharap. As we move toward the future, let us not forget that our eyes are how the world sees us. As AI and mathematics continue to work together, facial recognition has become increasingly useful in our society, from airport identification to security systems. However, as these technologies continue to advance, the future of facial recognition and the extent of its capabilities remains uncertain. While these innovations offer convenience and security, they also raise questions about our ability to protect ourselves. This is where our project, MATA-MATICS: Anti-Facial Recognition Makeup, comes in. Through creative alterations and unconventional designs, we aim to inform and explore alternative ways of challenging facial recognition systems and protecting our identities. Because as technology progresses, so must our ability to protect ourselves.