Filters are digital image effects often used on social media. They initially simulated the effects of camera filters, and they have since developed with facial recognition technology and computer-generated augmented reality. Social media filters—especially beauty filters—are often used to alter the appearance of selfies taken on smartphones or other similar devices. While filters are commonly associated with beauty enhancement and feature alterations, there is a wide range of filters that have different functions. From adjusting photo tones to using face animations and interactive elements, users have access to a range of tools. These filters allow users to enhance photos and allow room for creative expression and fun interactions with digital content. == History == Beauty filters originate from Purikura ("print club"), a type of Japanese photographic arcade game machine conceived in 1994 by Sasaki Miho, a female employee at Atlus, and released in 1995 by Atlus and Sega primarily for female visitors at Japanese arcades. They allowed the manipulation of digital selfie photos with kawaii beauty filters similar to later Snapchat filters. Purikura filters included beautifying the image, cat whiskers, bunny ears, writing text, scribbling graffiti, selecting backdrops, borders, insertable decorations, icons, hair extensions, twinkling diamond tiaras, tenderized light effects, and predesigned decorative margins. To capitalize on the Purikura phenomenon in Japan during the late 1990s, Japanese mobile phones began including a front-facing camera, starting with the Kyocera Visual Phone VP‑210 in 1999. The Sanyo SCP-5300 released in 2002 was the first camera phone with filter effects, such as illumination, white‑balance control, sepia, black and white, and negative colors. Purikura-like beauty filters later appeared in smartphone apps such as Instagram and Snapchat in the 2010s. In 2010, Apple introduced the iPhone 4—the first iPhone model with a front-facing camera. It gave rise to a dramatic increase in selfies, which could be touched up with more flattering lighting effects with applications such as Instagram. The American photographer Cole Rise was involved in the creation of the original filters for Instagram around 2010, designing several of them himself, including Sierra, Mayfair, Sutro, Amaro, and Willow. However, the technology for virtual lens filters was invented and patented by Patrick Levy-Rosenthal in 2007. The patent received 100 citations, including Facebook, Nvidia, Microsoft, Samsung, and Snap. In September, 2011, the Instagram 2.0 update for the application introduced "live filters," which allowed the user to preview the effect of the filter while shooting with the application's camera. #NoFilter, a hashtag label to describe an image that had not been filtered, became popular around 2013. An update in 2014 allowed users to adjust the intensity of the filters as well as fine-tune other aspects of the image, features that had been available for years on applications such as VSCO and Litely. In 2014, Snapchat started releasing sponsored filters to monetize the participatory use of the application. In September 2015, Snapchat acquired Looksery and released a feature called "lenses," animated filters using facial recognition technology. Some of the early lenses available on Snapchat at the time were Heart Eyes, Terminator, Puke Rainbows, Old, Scary, Rage Face, Heart Avalanche. The Coachella filter released April 2016 was a popular early augmented reality filter. In April 2017, Facebook released the Camera Effects Platform, which is the first augmented reality platform that allows developers to create their own filters and effects on Facebook's Camera. In December 2017, Snapchat also launched their Lens Studio augmented reality developer tool that allows users and advertisers to do the same on the Snapchat application. In April 2022,TikTok joined the two, and launched their own augmented reality developer platform called Effect house. In February 2023, Effect House gave opened up the access to generative AI tools that allowed creators to change facial features in real time. In November 2023, TikTok released a feature where users no longer needed Effect House to create their own filters, as they are now able to create their own effects on the TikTok application. In August 2024, Meta announced that it would be removing third-party filter effects from its family of apps by January 14, 2025. The AR development software Meta Spark AR will also be retired at the same time; it was at one point the "world's largest mobile AR platform". Brand and creator effects represent the vast majority of filters available on Meta platforms, with over 2 million third-party filters available as of 2021. == Beauty filter == A beauty filter is a filter applied to still photographs, or to video in real time, to enhance the physical attractiveness of the subject. Typical effects of such filters include smoothing skin texture and modifying the proportions of facial features, for example enlarging the eyes or narrowing the nose. Filters may be included as a built-in feature of social media apps such as Instagram or Snapchat, or implemented through standalone applications such as Facetune. In 2020, the "Perfect Skin" filter for Snapchat and Instagram which was created by Brazilian augmented reality developer Brenno Faustino gained more than 36 million impressions in the first 24 hours of its release. In 2021, TikTok users pointed out how the default front-facing camera on the platform automatically applied the retouch and other feature-altering filters. Users noted that these filters slimmed down faces, smoothed skin, whitened teeth, and altered facial features such as nose and eye size, without the option to disable this feature through settings. In March 2023, the "Bold Glamour" filter was released on TikTok and instantly went viral with over 18 million videos created within its first week. This filter subtly enhances the user's facial features seamlessly, giving the illusion of fuller eyebrows, taller cheekbones, enhanced eye make up, a smaller nose, plumper lips, and clearer skin, giving off a natural yet distinct effect. As of May 2024, the filter has been used in over 220 million videos and has become a pivotal moment for beauty filters on digital platforms. Critics have raised concerns that the widespread use of such filters on social media may lead to negative body image, particularly among girls. Though Meta's intention of removing third-party filters will likely see all beauty filters removed, academics feel that the damage of beautifying filters is already done. === Background === The manipulation of photos to enhance attractiveness has long been possible using software such as Adobe Photoshop and, before that, analogue techniques such as airbrushing. However, such tools required considerable technical and artistic skill, and so their use was mostly limited to professional contexts, such as magazines or advertisements. By contrast, filters work in an automated fashion through the use of complex algorithms, requiring little or no input from the user. This ease of use, in combination with the increase in processing power of smartphones, and the rise of social media and selfie culture, have led to photographic manipulation occurring on a much wider scale than ever before. One of the earliest examples of a content-aware digital photographic filter is red-eye reduction. === Effects === Typical changes applied by beauty filters include: Smoothing skin texture; minimizing fine lines and blemishes Erasing under-eye bags Erasing naso-labial lines ("laugh lines") Application of virtual makeup, such as lipstick or eyeshadow Slimming the face; erasing double chins Enlarging the eyes Whitening teeth Narrowing the nose Increasing fullness of the lips Beauty filters most frequently target the face, though in some cases they may affect other body parts. For example, the app "Retouch Me" was reported to have a feature which allows users to superimpose visible abdominal muscles (a "six pack") onto photos featuring the subject's bare stomach. === Reception and psychological effects === Some commentators have expressed concern that beauty filters may create unrealistic beauty standards, particularly among girls, and contribute to rates of body dysmorphic disorder. A correlation has been established between negative body image and the use of beautifying filters, though the direction of causation is unknown. The inability to discern whether a particular image has been filtered is thought to exacerbate their negative psychological effects. Policymakers have advocated for social networks to disclose the use of filters; TikTok, Instagram, and Snapchat all label filtered photos and videos with the name of the filter applied. It has also been noted that beauty filters on social media tend to highlight Eurocentric features, like lighter eyes, a smaller nose, and flushed ch
Fragment (computer graphics)
In computer graphics, a fragment is the data necessary to generate a single pixel's worth of a drawing primitive in the frame buffer. These data may include, but are not limited to: raster position depth interpolated attributes (color, texture coordinates, etc.) stencil alpha window ID As a scene is drawn, drawing primitives (the basic elements of graphics output, such as points, lines, circles, text etc.) are rasterized into fragments which are textured and combined with the existing frame buffer. How a fragment is combined with the data already in the frame buffer depends on various settings. In a typical case, a fragment may be discarded if it is further away than the pixel which is already at that location (according to the depth buffer). If it is nearer than the existing pixel, it may replace what is already there, or, if alpha blending is in use, the pixel's color may be replaced with a mixture of the fragment's color and the pixel's existing color, as in the case of drawing a translucent object. In general, a fragment can be thought of as the data needed to shade the pixel, plus the data needed to test whether the fragment survives to become a pixel (depth, alpha, stencil, scissor, window ID, etc.). Shading a fragment is done through a fragment shader (or pixel shaders in Direct3D). In computer graphics, a fragment is not necessarily opaque, and could contain an alpha value specifying its degree of transparency. The alpha is typically normalized to the range of [0, 1], with 0 denotes totally transparent and 1 denotes totally opaque. If the fragment is not totally opaque, then part of its background object could show through, which is known as alpha blending.
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Dilek Hakkani-Tür
Dilek Z. Hakkani-Tür is a Turkish-American computer scientist focusing on speech processing, speech recognition, and dialogue systems. She is a professor of computer science at the University of Illinois Urbana-Champaign. == Education and career == Hakkani-Tür is a 1994 graduate of Middle East Technical University in Ankara, Turkey. She continued her studies at Bilkent University, also in Ankara, where she earned a master's degree in 1996 and completed her Ph.D. in 2000. She worked as a researcher at AT&T Labs from 2001 to 2005, at the International Computer Science Institute from 2006 to 2010, at Microsoft Research from 2010 to 2016, at Google Research from 2016 to 2018, and at Amazon Alexa from 2018 to 2023. At Microsoft, she was in the team of scientists that built the first prototype of the Cortana virtual assistant. While working for Amazon Alexa, she also taught at the University of California, Santa Cruz as a distinguished visiting instructor. She joined the University of Illinois Urbana-Champaign faculty in 2023. She was editor-in-chief of IEEE/ACM Transactions on Audio, Speech and Language Processing from 2019 to 2021, and is president of the Special Interest Group on Discourse and Dialogue of the Association for Computational Linguistics for the 2023–2025 term. She has served as co-editor-in-chief of Transactions of the Association for Computational Linguistics since 2024. == Recognition == In 2014, Hakkani-Tür was elected as an IEEE Fellow "for contributions to spoken language processing", and as a Fellow of the International Speech Communication Association "for contributions to advancing the state-of-the-art in spoken language processing, especially for human/human and human/machine conversational understanding". In 2024, she was elected as a Fellow of the Association for Computational Linguistics for her contributions to spoken dialogue systems.
Hanna Hajishirzi
Hannaneh Hajishirzi is an Iranian-American computer scientist specializing in natural language processing. She is Torode Family Professor in Computer Science & Engineering in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, head of the H2Lab in the Allen School, and a senior director of natural language processing in the Allen Institute for AI. == Education and career == After a bachelor's degree from the Sharif University of Technology, Hajishirzi completed her Ph.D. in computer science in 2011, at the University of Illinois Urbana-Champaign. Her dissertation, Action-Centered Reasoning for Probabilistic Dynamic Systems, was supervised by Eyal Amir. After postdoctoral research at Disney Research in Pittsburgh, Hajishirzi joined the University of Washington in 2012, as a research scientist in electrical engineering. In 2015 she became a research assistant professor in electrical engineering. She obtained a regular-rank assistant professorship in 2018, at the same time becoming an AI Fellow in the Allen Institute for AI, where she became a senior director of research in 2021. She was promoted to associate professor in 2022 and to full professor in 2025. == Recognition == Hajishirzi was named as a Fellow of the Association for Computational Linguistics in 2025, "for significant contributions to question answering, scientific applications, multimodal artificial intelligence, and fully open language models". == Personal life == Hajishirzi is married to Ali Farhadi, the CEO of the Allen Institute for AI.
RIPAC (microprocessor)
RIPAC was a VLSI single-chip microprocessor designed for automatic recognition of the connected speech, one of the first of this use. The project of the microprocessor RIPAC started in 1984. RIPAC was aimed to provide efficient real-time speech recognition services to the italian telephone system provided by SIP. The microprocessor was presented in September 1986 at The Hague (Netherlands) at EUSPICO conference. It was composed of 70.000 transistors and structured as Harvard architecture. The name RIPAC is the acronym for "Riconoscimento del PArlato Connesso", that means "Recognition of the connected speech" in Italian. The microprocessor was designed by the Italian companies CSELT and ELSAG and was produced by SGS: a combination of Hidden Markov Model and Dynamic Time Warping algorithms was used for processing speech signals. It was able to do real-time speech recognition of Italian and many languages with a good affordability. The chip, issued by U.S. Patent No. 4,907,278, worked at first run.
Alexei A. Efros
Alexei "Alyosha" A. Efros (born 9 April 1975) is a Russian-American computer scientist and professor at University of California, Berkeley. He has contributed to the field of computer vision, and his work has been referenced in Wired, BBC News, The New York Times, and The New Yorker. == Early life and education == Efros was born in St. Petersburg in the Soviet Union. His father is Alexei L. Efros, then a physics professor at the Ioffe Physico-Technical Institute. His family emigrated to the United States when he was 14 to accommodate his father's career and the family settled in Salt Lake City in 1991. He graduated from the University of Utah in 1997, and attended University of California, Berkeley for his PhD, where he was advised by Jitendra Malik and graduated in 2003. He then spent a year as a research fellow at the University of Oxford, where he worked with Andrew Zisserman. == Career == Efros joined the faculty at Carnegie Mellon University in Pittsburgh, where he remained until 2013 when he joined the faculty of the University of California, Berkeley. He received a Guggenheim Fellowship in 2008. He received the 2016 ACM Prize in Computing.