face image recognition based on joint features

Hello Dear, I'm Mohamed saeed, mechanical and aeronautical engineering. I'm Matlab expert with high experience. specially face recognition system based on PCA. I'm flexible in price. I'm free and ready to work from now.

Dear, I have already read your job post in carefully and understood it well. I have good and full experiences on mathematics and algorithm, computation, numerical analysis, digital image processing, fingerprint/face recognition using C/C++, Matlab for 10+ years and I had worked on many matlab project before. So, I can complete your project within your timeline, finely. Please let me know about your project detail and inform me. I am waiting for your good reply. Thanks and regards, Xie

sir i can help u with this project i have done masters in computer engg with specilization in image processing and pattern recognition this is a dummy bid i can give u an exact estimate after we discuss the project hopping for your kind consideration

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I do High end academic projects, advanced research projects, software projects, web designing projects, all engineering projects. I Worked with the US, UK, UAE, AUS, ASIA clients and so can understand their requirements well. CDR Writing, HND Projects, MATLAB, PSPICE, NS2, NS3 Simulation Projects are my favorites.

I can start ur work immediately. I have 6 yrs experience as Senior Research Engineer in telecom domain, I have done design and coding in 1. MATLAB 2. LabVIEW 3 VHDL,Verilog 4. Micro controller coding 5. DSP,DIP

The basic idea is as follows. We first train the joint feature learning model on small face patches and then use the learned feature weighting matrices to filter sampled small patches within large patches.2 Within each large image patch, the outputs of small patches are concatenated and taken as the input of the next layer. We employ weighted PCA (WPCA) [38] to map the combined responses of the first layer to a low-dimensional feature space to reduce the redundancy. Similarly to most deep learning methods [29], [34], [35], [50], the stacked joint feature learning network is trained greedily layer-wise. Specifically, we train the first layer until convergence before training the second layer. Having learned feature extraction matrices for the first and second layers, we project samples from each layer with the learned feature projections and learn dominant patterns to generate the codebook for each region and layer, respectively. In our experiments, we combine features extracted from both layers and combine them together for face recognition. In the experiment section, we show that this combination works better than using feature extracted from a single individual layer.

Hello, Thank you for posting a project here on www.freelancer.com. I have briefly checked the description, and I can deliver as per the requirements however I need us to discuss for more clarity on the details, deadline and budget as well. Please reply via the CHAT button to activate the conversation or the green AWARD button for me to start right away. Thanks and welcome.

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