Academia.eduAcademia.edu

Figure 11 – uploaded by salem almarri

See full PDFdownloadDownload figure
Figure 1.11: Hyperplane presentation of decision boundary for a two-class pattern classification youndary, which is a hyperspace; the line that separates the two classes from each other. 1.2.1 The Perception Convergence Theorem

Figure 1 11: Hyperplane presentation of decision boundary for a two-class pattern classification youndary, which is a hyperspace; the line that separates the two classes from each other. 1.2.1 The Perception Convergence Theorem

Related Figures (48)

Fig 1.1 depicts a schematic of model of a single neuron. The mathematical relationships describing the neuron model are given in equations 1.1
Fig 1.1 depicts a schematic of model of a single neuron. The mathematical relationships describing the neuron model are given in equations 1.1
Table 1.1: Notations used to describe a nonlinear model of a neuron.
Table 1.1: Notations used to describe a nonlinear model of a neuron.
Figure 1.2: Result of affine transformation of external bias input on neuron k on output Ux.
Figure 1.2: Result of affine transformation of external bias input on neuron k on output Ux.
Figure 1.3: Threshold function and a Sigmoid function for smooth, continuous range of values between -1 and 1 to predict outputs probability ir
Figure 1.3: Threshold function and a Sigmoid function for smooth, continuous range of values between -1 and 1 to predict outputs probability ir
Figure 1.4: Feedforward network with a single layer of neurons. layer. No computation is made by the input layer of source nodes.
Figure 1.4: Feedforward network with a single layer of neurons. layer. No computation is made by the input layer of source nodes.
Figure 1.5: Fully connected feedforward multi-layer network with one hidden layer and one output layer. the network.
Figure 1.5: Fully connected feedforward multi-layer network with one hidden layer and one output layer. the network.
Figure 1.6: Time response of a system to a feedforward weight w stable, linear divergent, or exponential divergent. This is illustrated in Fig 1.6. Depending on the value of the feedforward weight w, which can be less than one, equal
Figure 1.6: Time response of a system to a feedforward weight w stable, linear divergent, or exponential divergent. This is illustrated in Fig 1.6. Depending on the value of the feedforward weight w, which can be less than one, equal
Figure 1.8: Transition pathway of data pyramid
Figure 1.8: Transition pathway of data pyramid
Figure 1.9: Rosenblatt’s Perceptron
Figure 1.9: Rosenblatt’s Perceptron
Figure 1.10: Signal-flow graph of a perceptron induced local field or activation potential v (equation 1.10), which is then applied to a hard
Figure 1.10: Signal-flow graph of a perceptron induced local field or activation potential v (equation 1.10), which is then applied to a hard
Figure 2.1: Convolutional operation
Figure 2.1: Convolutional operation
Figure 2.2: Pooling operation element along the column until the whole matrix is pooled.
Figure 2.2: Pooling operation element along the column until the whole matrix is pooled.
Figure 2.3: Fully connected layer end of the CNN architecture, or somewhere intermediate. connected layer as input as shown in Fig 2.3. The fully connected layer is usually placed at the
Figure 2.3: Fully connected layer end of the CNN architecture, or somewhere intermediate. connected layer as input as shown in Fig 2.3. The fully connected layer is usually placed at the
Figure 2.4: Representation of sentiment analysis of deep convolutional neural network something as negative, neutral or positive. This is illustrated in Fig 2.4.
Figure 2.4: Representation of sentiment analysis of deep convolutional neural network something as negative, neutral or positive. This is illustrated in Fig 2.4.
Figure 3.1: Representation of facial action coding system
Figure 3.1: Representation of facial action coding system
actions. state, but a complex chain of events that once simulated would evoke feelings resulting ir
actions. state, but a complex chain of events that once simulated would evoke feelings resulting ir
cube [23]. The three signal substances are dopamine, noradrenaline and serotonin. This is a three-dimensional model (shown in Fig 3.3) with signal substances that form
cube [23]. The three signal substances are dopamine, noradrenaline and serotonin. This is a three-dimensional model (shown in Fig 3.3) with signal substances that form
Figure 5.1: Deep learning, machine learning, and artificial intelligence
Figure 5.1: Deep learning, machine learning, and artificial intelligence
Figure 5.2: Extracted features of 8 channels (labels) of layers 3, layer 8 and layer 14 as visualized by Deep Dream feature visualization technique
Figure 5.2: Extracted features of 8 channels (labels) of layers 3, layer 8 and layer 14 as visualized by Deep Dream feature visualization technique
Table 6.1: Database properties of altered JAFFE Database Table 6.2: Fast R-CNN training options using JAFFE Database layer. The network was then trained with training options of Table 6.2
Table 6.1: Database properties of altered JAFFE Database Table 6.2: Fast R-CNN training options using JAFFE Database layer. The network was then trained with training options of Table 6.2
Figure 6.1: Image samples obtained from JAFFE database [1]
Figure 6.1: Image samples obtained from JAFFE database [1]
[able 6.3: JAFFE trained Fast R-CNN benchmarked against other published work
[able 6.3: JAFFE trained Fast R-CNN benchmarked against other published work
Figure 6.2: Detection precision analysis of Fast R-CCN (VGG-16) trained on JAFFE
Figure 6.2: Detection precision analysis of Fast R-CCN (VGG-16) trained on JAFFE
Table 6.4 Database properties of facial expression recognition challenge 2013
Table 6.4 Database properties of facial expression recognition challenge 2013
Table 6.6: Database properties of altered FER-2013 Database Table 6.7: Fast R-CNN training options using FER-2013 Database yerformance analysis is depicted in Fig 6.4.
Table 6.6: Database properties of altered FER-2013 Database Table 6.7: Fast R-CNN training options using FER-2013 Database yerformance analysis is depicted in Fig 6.4.
Figure 6.4: Detection precision analysis of Fast R-CCN (VGG-16) trained on FER-2013
Figure 6.4: Detection precision analysis of Fast R-CCN (VGG-16) trained on FER-2013
Table 6.8: FER-2013 trained Fast R-CNN benchmarked against other published work 6.3 RAVDESS Database Experiment
Table 6.8: FER-2013 trained Fast R-CNN benchmarked against other published work 6.3 RAVDESS Database Experiment
Table 6.9: RAVDESS database processing configuration Table 6.10: Fast R-CNN training options using RAVDESS database
Table 6.9: RAVDESS database processing configuration Table 6.10: Fast R-CNN training options using RAVDESS database
Figure 6.5: Samples of processed images from RAVDESS database [3].
Figure 6.5: Samples of processed images from RAVDESS database [3].
Table 6.11: Fast R-CNN training options using RAVDESS database for three different network models. Once the network model has been trained on the 50% of the processed data from Table each network.
Table 6.11: Fast R-CNN training options using RAVDESS database for three different network models. Once the network model has been trained on the 50% of the processed data from Table each network.
VGG-16 network model is shown to be performing poorly when compared to VGG-19 Table 6.12: RAVDESS trained Fast R-CNN benchmarked against other published work
VGG-16 network model is shown to be performing poorly when compared to VGG-19 Table 6.12: RAVDESS trained Fast R-CNN benchmarked against other published work
Figure 6.7: Average miss rate analysis on three different network models
Figure 6.7: Average miss rate analysis on three different network models
Figure 6.8: An undertrained network model, trained with max epochs of 3 (left) and an overtrained network model trained with max epochs of 30. (right).
Figure 6.8: An undertrained network model, trained with max epochs of 3 (left) and an overtrained network model trained with max epochs of 30. (right).
Figure 6.9: Detected and classified facial emotion using trained Fast R-CNN object detector (right), in comparison with a facial expression obtained from the training dataset (left).
Figure 6.9: Detected and classified facial emotion using trained Fast R-CNN object detector (right), in comparison with a facial expression obtained from the training dataset (left).
Figure 6.10: Percentage of detected emotions expressed by 100 different participants tector was more confident that it was a fearful expression rather than a surprised one.
Figure 6.10: Percentage of detected emotions expressed by 100 different participants tector was more confident that it was a fearful expression rather than a surprised one.
Figure 6.11: Trained Fast R-CNN classifying 5 expressed emotions correctly.
Figure 6.11: Trained Fast R-CNN classifying 5 expressed emotions correctly.
Figure 6.12: Rapidly protoyped system platform for Fast R-CNN deployment
Figure 6.12: Rapidly protoyped system platform for Fast R-CNN deployment
per the neural network’s specification. This code is also able to process all images within
per the neural network’s specification. This code is also able to process all images within
webcam to detect any face. Once the object detector recognizes a human face, the image is
webcam to detect any face. Once the object detector recognizes a human face, the image is
51]. This way image features learned by the network can be visualized and understood.
51]. This way image features learned by the network can be visualized and understood.
Related topics:
Computer ScienceArtificial IntelligenceFacial emotion recognition
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved