A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

Benradi, Hicham; Chater, Ahmed; Lasfar, Abdelali

doi:10.11591/ijai.v12.i2.pp627-640

Cited by 9 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then we make the combined feature to go through a Dense layer with activation "ReLu" to learn and summarize the relationship between the two features [11]:…”

Section: Combine the Two Partsmentioning

confidence: 99%

See 1 more Smart Citation

Image Caption using VGG model and LSTM

2024

ACE

View full text Add to dashboard Cite

Deep convolutional networks and recurrent neural networks have gained significant popularity in the field of image captioning tasks in recent times. As we all know the performance and the architecture of models are still eternal topic. We constructed the model using a new method to enhance its performance and accuracy. In our model, we make use of pretrained CNN model VGG (Visual Geometry Group) to extract image features, and learn caption sentence features using bidirectional LSTM(Long-Short-Term-Memory) which can better understand the meaning of sentences in the text. Then we combine the image features and caption features to predict captions for images. The dataset Flickr8K is used to train and test the model. Additionally, the model has the ability to produce captions that are shorter than a specified caption length. We evaluated our model with Bilingual Evaluation Understudy (BLEU) score which measures the similarity of predicted text and to the real text. After evaluation and comparison, our model is proved to be well-done on some conditions.

show abstract

“…Then we make the combined feature to go through a Dense layer with activation "ReLu" to learn and summarize the relationship between the two features [11]:…”

Section: Combine the Two Partsmentioning

confidence: 99%

“…Then, the output of the Dense layer will be passed into another Dense layer with activation "Softmax", and vocab size nodes to get result which is the probability distribution of the prediction for the next word [11]:…”

Section: Combine the Two Partsmentioning

confidence: 99%

Image Caption using VGG model and LSTM

2024

ACE

View full text Add to dashboard Cite

show abstract

“…Its role is to measure the divergence between the probability distribution predicted by a model and the actual distribution of classes by computing the output probability of a model and the labels that correspond to the classes. This operation is performed using the following equation: (10) Where j(θ) presents the total loss calculated from the predictions of the model, y i represents the true value of class i(0 or 1) and ŷi represents the probability predicted for class i by a model.…”

Section: Convolutional Neural Network Architecture Cnnmentioning

confidence: 99%

“…However, the presence of variance that can occur in several forms (lighting, orientation, pose, accessories, etc.) in an image can affect facial recognition, since facial recognition algo-rithms need a clear, sharp image of the face to identify unique features and compare them with a database of recorded faces [10]. This is why it is important to take variance into account when designing and evaluating facial recognition algorithms, and to ensure that they are capable of handling a wide variety of situations and image conditions to guarantee accurate and reliable identification.…”

Section: Introductionmentioning

confidence: 99%

A combined method based on CNN architecture for variation-resistant facial recognition

Benradi,

Chater,

Lasfar

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset.

show abstract

“…For the calculation of the descriptor vector in the proximity. There are many techniques such as scale invariant feature transform (SIFT) [14], shape contexts [15], and speed up robust features (SURF), to name a few [6].…”

Section: Feature Extractionmentioning

confidence: 99%

New approach to similarity detection by combining technique three-patch local binary patterns (TP-LBP) with support vector machine

Chater

Benradi

Lasfar

2023

IJ-AI

View full text Add to dashboard Cite

Recognition systems have received a lot of attention because of their various uses in people's daily lives, for example in robotic intelligence, smart cameras, security surveillance or even criminal identification. Determining the similarity of faces by different face variations is based on robust algorithms. The validation of our experiment is done on two sets of data. In this paper, we compare two facial recognition system techniques according to the recognition rate and the average authentication time: in order to increase the accuracy rate and decrease the processing time. our approach is based on feature extraction by two algorithms principal components analysis scaleinvariant feature transform (PCA-SIFT) and speeded up robust features (SURF), then uses the random sample consensus (RANSAC) technique to cancel outliers. Finally, face recognition is established on the basis of proximity determination. The second technique is based on the association of support vector machine (SVM) classifier with the key point recovery technique. the results obtained by the second technique is better for both databases: The recognition rate of the base olivetti research laboratory (ORL) should be 98.125800 and that of the Grimace base 97.2851500. The evaluation according to the time of the second technique does not exceed 300ms on average.

show abstract

A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques

Cited by 9 publications

References 21 publications

Image Caption using VGG model and LSTM

Image Caption using VGG model and LSTM

A combined method based on CNN architecture for variation-resistant facial recognition

New approach to similarity detection by combining technique three-patch local binary patterns (TP-LBP) with support vector machine

Contact Info

Product

Resources

About