1. A computer implemented method to classify camera images, comprising: a. generating a baseline gender model and an age estimation model using one or more convolutional neural networks (CNNs); b. capturing correspondences of faces by face tracking; c. applying a stochastic update rule for each face image determined as Ω - γ t ⁡ [ λ ⁡ ( Ω - Ω 0 ) + ( F ⁡ ( X t , Ω ) - y t ~ ) ⁢ ∂ ∂ Ω ⁢ F ⁡ ( X , θ ) ] , where y t ~ = 1  T ⁡ ( X t )  ⁢ ∑ Z ∈ T ⁡ ( X t ) ⁢ F ⁡ ( Z , Ω ) , γ t is a step size, term λ(Ω−Ω 0 ) represents weight decay, and a baseline neural network function is y=F(X,Ω 0 ), and d. applying incremental learning to the CNNs and enforcing correspondence constraint such that CNN outputs are consistent and stable for one person.
1. A computer implemented method to classify camera images, comprising: a. generating a baseline gender model and an age estimation model using one or more convolutional neural networks (CNNs); b. capturing correspondences of faces by face tracking; c. applying a stochastic update rule for each face image determined as Ω - γ t ⁡ [ λ ⁡ ( Ω - Ω 0 ) + ( F ⁡ ( X t , Ω ) - y t ~ ) ⁢ ∂ ∂ Ω ⁢ F ⁡ ( X , θ ) ] , where y t ~ = 1  T ⁡ ( X t )  ⁢ ∑ Z ∈ T ⁡ ( X t ) ⁢ F ⁡ ( Z , Ω ) , γ t is a step size, term λ(Ω−Ω 0 ) represents weight decay, and a baseline neural network function is y=F(X,Ω 0 ), and d. applying incremental learning to the CNNs and enforcing correspondence constraint such that CNN outputs are consistent and stable for one person. 10. The method of claim 1 , comprising applying weakly supervised incremental training to face correspondences.