Face recognition: a convolutional neural-network approach

By Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back. 1997

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Citations in the corpus

Recent advances in convolutional neural networks (2015, methodology): “CNNs have been applied in image classiﬁcation for a long time [88–91].”
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, methodology): “CNN is mostly used in image and video processing, classiﬁcation and recognition processes [17, 12, 21, 6], sentence classiﬁcation, and natural language process [16, 13].”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “CNN is widely being used in image recognition [242], drug discovery [243], and anomaly detection [244], [245], to name a few.”
Deep Learning for Computer Vision: A Brief Review (2018, background): “The first work employing CNNs for face recognitionwas [80]; today light CNNs [81] and VGG Face Descriptor [82] are among the state of the art.”
Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach (2016, background): “1) Brief Review of CNN: A CNN [27] is usually composed of alternate convolutional and max-pooling layers (denoted as C layers and P layers) to extract hierarchical features from the original inputs (receptive field), subsequently with several fully connected layers (denoted by FC layers) followed to”
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities (2018, methodology): “Deep learning — including Recurrent Neural Networks (RNN) [12], [13], [14], [15], [16], Convolutional Neural Networks (CNN) [17], [18], and Deep Belief Networks (DBN) [19], [20] — has been successful in image and natural language processing.”
Artificial intelligence in recommender systems (2020, background): “Convolutional neural networks (CNN) [35] are capable of processing images and visual information.”
Analogue signal and image processing with large memristor crossbars (2017, background): “The edge extractions described in this step are also a frequent layer of convolutional neural networks (CNNs or ConvNets) 18,49,50 , which is the most computationally expensive step in the networks.”
Benchmarking State-of-the-Art Deep Learning Software Tools (2016, methodology): “Starting from the LeNet architecture, CNNs have accomplished a lot of successful tasks including ImageNet classification [14], face recognition [22], and object detection [23].”
Face anti-spoofing using patch and depth-based CNNs (2017, methodology): “However, compared to other face related problems, such as face recognition [27, 43] and face alignment [20], there are still substantially less efforts and exploration on face anti-spoofing using deep learning techniques.”
An Introductory Review of Deep Learning for Prediction Models With Big Data (2020, background): “Another useful activation function is the softmax function (Lawrence et al., 1997): (2) The softmax maps a n -dimensional vector x into a n -dimensional vector y having the property i y i = 1.”
Boosting Local Binary Pattern (LBP)-Based Face Recognition (2004, methodology): “While regarding classification methods, nearest neighbor [15], convolutional neural networks [ 6 ], nearest feature line [7], Bayesian classification [9] and Ad-”
Convexified Convolutional Neural Networks (2016, background): “, 2012), face recognition (Lawrence et al., 1997), speech recognition (Hinton et al.”
A robot exploration strategy based on Q-learning network (2016, methodology): “This supervised learning model is implemented with a Convolution Neural Network [4].”