Face Recognition

By Filareti Tsalakanidou, Sotiris Malassiotis, Michael Gerasimos Strintzis. 2018

564 citations recorded by the source corpus.

Face recognition technology is a biometric technology that uses a person's face traits to identify them. Face photos are collected by people, and the recognition technology processes them automatically. The study introduces related face recognition research from severalangles. The paper discusses the stages of facial recognition development and related technologies. We discuss face recognition research in real- world scenarios, as well as general evaluation standards and face recognition databases.We take a look at facial recognition from the future. Face recognition has emergedas a promising future development direction with numerous possible applications. Nowadays, face recognition is one of the most advanced computer vision technologies. In computer vision, illumination, and other fields, face recognition is always a difficult issue. facial expression, posture Face recognition keeps track of the objects that are being tracked.in video photos captured with a video camera in real time Simply said, To put it another way, it's a system application for automatically recognising people. a person captured in a photograph or video frame In this paper, we will discuss to propose a facial recognition system that is automated. This Face detection, feature extraction, and facial recognition are all used in this application. algorithmic recognition, which automatically detects when the individual in front of the camera has a human face knowing who he is KEYWORD-Face Recognition, Principal component analysis (PCA), Biometrics, eigenfaces.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning (2017, background): “Face recognition systems have been widely used for a variety of purposes, such as user authentication and video surveillance [47], [50], [51].”
Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016, background): “These systems are widely used for various sensitive purposes, including surveillance and access control [26, 27, 28].”
Model Vulnerability to Distributional Shifts over Image Transformation Sets (2019, methodology): “We test our search methods on a widely used API for face detection [1], that takes RGB images as input and provides in output the locations of the faces in the image.”
1 Imitation and Other Minds: The "Like Me" Hypothesis (2005, background): “…data show that visual displays of parts of the face and hands activate specific brain sites in monkeys (Desimone, 1991; Gross, 1992; Gross & Sergent, 1992; Jellema et al., 2002; Perrett et al., 1992; Rolls, 1992) and related work is emerging in human studies (Buccino et al., 2001).”
User re-authentication via mouse movements (2004, background): “, a fingerprint [35], a palm print [32], a voice sample [12], an iris pattern [5], which are referred to as biometrics [3]).”
Recent advances in visual and infrared face recognition - a review (2005, methodology): “In [131], the image is scanned in a zigzag fashion to form a 1D observation sequence.”
Three-Dimensional Face Recognition (2005, methodology): “Achermann et al. (1997), Hesher et al. (2003), Mavridis et al. (2001), Chang et al. (2003), and Tsalakanidou et al. (2003) explored the extension of conventional dimensionality reduction techniques, like Principal Component Analysis (PCA), to range images or combination of intensity and range…”
Face recognition using Histograms of Oriented Gradients (2011, methodology): “A nearest neighbor classifier with Euclidean and cosine distances is used as classifier (no other classifier was used to be able compare results with (Albiol et al., 2008)).”
LightFace: A Hybrid Deep Face Recognition Framework (2020, background): “Even though some out-of-box face recognition packages exist in the market such as face-recognition [23], there is no software package wrapping all of those state-of-the-art face recognition models yet.”
The impact of age-related variables on facial comparisons with images of children: algorithm and practitioner performance (2018, background): “Similarly, human studies have also shown that own-ethnicity bias exists amongst humans conducting facial comparison tasks (Hole & Bourne, 2010; Rhodes, 2013).”
Robust representations for face recognition: the power of averages. (2005, background): “In a more recent test of modern commercial systems (FERET FRVT2002; Phillips et al., 2003), the best available systems scored only 73% on a recognition test using a real-world database of images, even though these were consistent in quality, and taken in known lighting conditions.”